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download_analysis
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| Command: python hf_download_model.py | |
| Loading data from hf_quant_dataset.csv (use --retry for failed rows, --fetch-all to re-fetch everything) | |
| Dataset: 452 quantized models with known base date and model size | |
| Hubs: {'RedHatAI': 379, 'nvidia': 73} | |
| Formats: {'fp8': 180, 'w4a16': 75, 'w8a8': 74, 'nvfp4': 65, 'w8a16': 33, 'deepsparse': 8, 'int4': 6, 'gguf': 6, 'unknown': 2, 'gptq': 1, 'fp4-qad': 1, 'fp4-eagle3': 1} | |
| Prior variant exists: {1: 237, 0: 215} | |
| ====================================================================== | |
| Model 1: All models — hub, format, size, base popularity | |
| R² = 0.4129 (n = 452) | |
| ====================================================================== | |
| Feature Coefficient Interpretation | |
| -------------------------------------------------------------------------------- | |
| (intercept) 2.6371 baseline log-downloads | |
| quant_format_int4 -9.1491 -100.0% vs baseline category | |
| hub_nvidia 2.2113 +812.8% vs baseline category | |
| quant_format_w8a16 -1.9376 -85.6% vs baseline category | |
| quant_format_nvfp4 1.8961 +566.0% vs baseline category | |
| quant_format_gguf -1.8140 -83.7% vs baseline category | |
| quant_format_fp8 0.6878 +98.9% vs baseline category | |
| log_base_downloads 0.4158 +51.56% downloads per unit increase | |
| quant_format_w4a16 0.3209 +37.8% vs baseline category | |
| quant_format_w8a8 -0.1884 -17.2% vs baseline category | |
| quant_format_infrequent_sklearn -0.0837 -8.0% vs baseline category | |
| days_since_base -0.0092 -0.92% downloads per unit increase | |
| model_size_b -0.0010 -0.10% downloads per unit increase | |
| days_since_release 0.0006 +0.06% downloads per unit increase | |
| ====================================================================== | |
| Top 10 over-performers (actual >> predicted) | |
| ====================================================================== | |
| hub name downloads predicted_dl quant_format days_since_base | |
| RedHatAI Llama-2-7b-chat-quantized.w8a8 14378 11 w8a8 368 | |
| RedHatAI Llama-2-7b-chat-quantized.w8a16 2589 1 w8a16 359 | |
| RedHatAI Qwen2.5-1.5B-quantized.w8a8 6618774 12408 w8a8 24 | |
| nvidia NVIDIA-Nemotron-3-Nano-4B-GGUF 69030 201 gguf 308 | |
| RedHatAI Mixtral-8x7B-Instruct-v0.1-FP8 154925 663 fp8 452 | |
| nvidia NVIDIA-Nemotron-3-Super-120B-A12B-FP8 2232273 10318 fp8 4 | |
| RedHatAI Llama-Guard-4-12B-quantized.w4a16 83462 441 w4a16 299 | |
| nvidia NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 5547346 34546 nvfp4 4 | |
| RedHatAI Llama-3.2-1B-Instruct-FP8-dynamic 11682743 77108 fp8 7 | |
| RedHatAI gemma-4-31B-it-FP8-block 4358837 31112 fp8 23 | |
| ====================================================================== | |
| Top 10 under-performers (actual << predicted) | |
| ====================================================================== | |
| hub name downloads predicted_dl quant_format days_since_base | |
| nvidia Wan2.2-T2V-A14B-Diffusers-NVFP4 0 22844 nvfp4 289 | |
| nvidia Wan2.2-T2V-A14B-Diffusers-FP8 0 6823 fp8 289 | |
| RedHatAI Qwen2.5-Math-7B-FP8-dynamic 16 10811 fp8 73 | |
| RedHatAI Qwen2.5-32B-FP8-dynamic 18 11141 fp8 74 | |
| RedHatAI Qwen2.5-32B-quantized.w4a16 21 11566 w4a16 33 | |
| RedHatAI QwQ-32B-Preview-FP8-dynamic 26 9848 fp8 37 | |
| RedHatAI Qwen2.5-1.5B-Instruct-FP8-dynamic 167 58366 fp8 71 | |
| RedHatAI Qwen2.5-32B-quantized.w8a8 22 7595 w8a8 24 | |
| RedHatAI Qwen2.5-72B-quantized.w4a16 18 5219 w4a16 32 | |
| RedHatAI Qwen3-VL-32B-Instruct-FP8-block 108 17619 fp8 52 | |
| ====================================================================== | |
| Mean downloads by quant format | |
| ====================================================================== | |
| count mean median | |
| quant_format | |
| gptq 1 1388401 1388401 | |
| nvfp4 65 852222 91426 | |
| fp8 180 364859 17039 | |
| w8a8 74 134433 5544 | |
| w4a16 75 104454 8164 | |
| unknown 2 98039 98039 | |
| gguf 6 11620 136 | |
| w8a16 33 11413 776 | |
| fp4-qad 1 4834 4834 | |
| int4 6 338 0 | |
| deepsparse 8 291 281 | |
| fp4-eagle3 1 260 260 | |
| ====================================================================== | |
| Model 2: RedHatAI only — format × period interaction | |
| R² = 0.4066 (n = 379) | |
| ====================================================================== | |
| Feature Coef Multiplier Interpretation | |
| ---------------------------------------------------------------------------------------------------- | |
| (intercept) 0.414 baseline | |
| format_x_period_w4a16:2026H1 3.885 48.66 ×48.66 vs baseline | |
| format_x_period_fp8:2024H1 3.842 46.62 ×46.62 vs baseline | |
| format_x_period_nvfp4:2026H1 3.818 45.52 ×45.52 vs baseline | |
| format_x_period_nvfp4:2025H2 3.735 41.88 ×41.88 vs baseline | |
| format_x_period_nvfp4:2025H1 3.187 24.23 ×24.23 vs baseline | |
| format_x_period_fp8:2026H1 3.097 22.14 ×22.14 vs baseline | |
| format_x_period_fp8:2025H1 3.003 20.14 ×20.14 vs baseline | |
| format_x_period_w8a8:2025H1 2.920 18.55 ×18.55 vs baseline | |
| format_x_period_w4a16:2025H1 2.865 17.54 ×17.54 vs baseline | |
| format_x_period_fp8:2024H2 2.524 12.48 ×12.48 vs baseline | |
| format_x_period_infrequent_sklearn 2.202 9.04 ×9.04 vs baseline | |
| format_x_period_w4a16:2024H2 2.021 7.55 ×7.55 vs baseline | |
| format_x_period_w4a16:2025H2 1.681 5.37 ×5.37 vs baseline | |
| format_x_period_w8a8:2024H2 1.581 4.86 ×4.86 vs baseline | |
| format_x_period_fp8:2025H2 1.385 3.99 ×3.99 vs baseline | |
| format_x_period_gguf:2024H2 -1.296 0.27 ×0.27 vs baseline | |
| within_1_month 1.185 3.27 ×3.27 when released within 1 month of base | |
| format_x_period_w8a8:2026H1 1.121 3.07 ×3.07 vs baseline | |
| within_1_day 1.055 2.87 ×2.87 when released within 1 day of base | |
| within_1_week 0.861 2.37 ×2.37 when released within 1 week of base | |
| log_base_downloads 0.380 1.46 +46.22% per unit | |
| format_x_period_w8a16:2024H2 0.177 1.19 ×1.19 vs baseline | |
| days_since_base -0.002 1.00 -0.21% per unit | |
| model_size_b -0.002 1.00 -0.18% per unit | |
| ====================================================================== | |
| Format interest over time (coefficient = log-multiplier on downloads) | |
| Positive = more downloads than format average, negative = fewer | |
| ====================================================================== | |
| Format 2024H1 2024H2 2025H1 2025H2 2026H1 | |
| ----------------------------------------------------------------- | |
| fp8 3.84 2.52 3.00 1.38 3.10 | |
| gguf — -1.30 — — — | |
| nvfp4 — — 3.19 3.73 3.82 | |
| w4a16 — 2.02 2.86 1.68 3.88 | |
| w8a16 — 0.18 — — — | |
| w8a8 — 1.58 2.92 — 1.12 | |
| ====================================================================== | |
| Same table as download multiplier (1.00 = neutral) | |
| ====================================================================== | |
| Format 2024H1 2024H2 2025H1 2025H2 2026H1 | |
| ----------------------------------------------------------------- | |
| fp8 46.62 12.48 20.14 3.99 22.14 | |
| gguf — 0.27 — — — | |
| nvfp4 — — 24.23 41.88 45.52 | |
| w4a16 — 7.55 17.54 5.37 48.66 | |
| w8a16 — 1.19 — — — | |
| w8a8 — 4.86 18.55 — 3.07 | |
| ====================================================================== | |
| Sample counts per format × period | |
| ====================================================================== | |
| year_half 2024H1 2024H2 2025H1 2025H2 2026H1 | |
| quant_format | |
| deepsparse 8 0 0 0 0 | |
| fp8 12 52 37 29 23 | |
| gguf 0 5 0 0 0 | |
| gptq 1 0 0 0 0 | |
| nvfp4 0 0 5 14 11 | |
| w4a16 0 24 36 11 4 | |
| w8a16 0 33 0 0 0 | |
| w8a8 0 37 26 2 9 | |
| ====================================================================== | |
| Model 3: RedHatAI only — prior variant exists (no interactions, no timing) | |
| R² = 0.3349 (n = 379) | |
| ====================================================================== | |
| Feature Coef Multiplier Interpretation | |
| ---------------------------------------------------------------------------------------------------- | |
| (intercept) 3.429 baseline | |
| quant_format_infrequent_sklearn 3.373 29.17 ×29.17 vs baseline | |
| quant_format_gguf -2.762 0.06 ×0.06 vs baseline | |
| quant_format_w8a16 -1.888 0.15 ×0.15 vs baseline | |
| quant_format_nvfp4 1.630 5.11 ×5.11 vs baseline | |
| quant_format_fp8 0.649 1.91 ×1.91 vs baseline | |
| quant_format_w4a16 0.346 1.41 ×1.41 vs baseline | |
| log_base_downloads 0.327 1.39 +38.73% per unit | |
| total_variants_count 0.277 1.32 +31.93% per additional variant (RH+nvidia) | |
| prior_variant_exists -0.238 0.79 ×0.79 when any prior variant exists | |
| prior_same_format_exists 0.083 1.09 ×1.09 when same format already released | |
| quant_format_w8a8 -0.071 0.93 ×0.93 vs baseline | |
| days_since_base -0.008 0.99 -0.82% per unit | |
| model_size_b -0.003 1.00 -0.26% per unit | |
| days_since_prior_variant -0.001 1.00 -0.09% per day since prior variant published | |
| ====================================================================== | |
| Downloads by first-mover status (RedHatAI) | |
| ====================================================================== | |
| First quant variant for base: n=182, mean=293,267, median=8,064 | |
| Prior variant existed: n=197, mean=138,678, median=7,881 | |
| First of this quant format for base: n=345, mean=208,504, median=7,422 | |
| Same format already existed: n=34, mean=257,657, median=23,134 | |
| ====================================================================== | |
| Model 4: RedHatAI only — format × size + prior variant exists + timing | |
| R² = 0.4010 (n = 379) | |
| ====================================================================== | |
| Feature Coef Multiplier Interpretation | |
| ---------------------------------------------------------------------------------------------------- | |
| (intercept) -0.113 baseline | |
| format_x_size_nvfp4:15-50B 3.411 30.29 ×30.29 vs baseline | |
| format_x_size_nvfp4:5-15B 3.216 24.93 ×24.93 vs baseline | |
| format_x_size_w4a16:5-15B 2.743 15.53 ×15.53 vs baseline | |
| format_x_size_fp8:15-50B 2.677 14.54 ×14.54 vs baseline | |
| format_x_size_w8a8:5-15B 2.627 13.84 ×13.84 vs baseline | |
| format_x_size_fp8:5-15B 2.591 13.35 ×13.35 vs baseline | |
| format_x_size_fp8:<5B 2.418 11.23 ×11.23 vs baseline | |
| format_x_size_w4a16:15-50B 2.391 10.93 ×10.93 vs baseline | |
| format_x_size_w8a8:<5B 2.167 8.73 ×8.73 vs baseline | |
| format_x_size_w8a16:5-15B 1.916 6.79 ×6.79 vs baseline | |
| format_x_size_w4a16:<5B 1.839 6.29 ×6.29 vs baseline | |
| within_1_day 1.638 5.14 ×5.14 when released within 1 day of base | |
| format_x_size_w8a16:<5B -1.392 0.25 ×0.25 vs baseline | |
| within_1_month 1.377 3.96 ×3.96 when released within 1 month of base | |
| format_x_size_w8a8:15-50B 1.139 3.13 ×3.13 vs baseline | |
| format_x_size_gguf:<5B -0.658 0.52 ×0.52 vs baseline | |
| within_1_week 0.570 1.77 ×1.77 when released within 1 week of base | |
| log_base_downloads 0.372 1.45 +45.04% per unit | |
| format_x_size_w8a16:15-50B -0.302 0.74 ×0.74 vs baseline | |
| prior_variant_exists 0.210 1.23 ×1.23 when any prior variant exists | |
| total_variants_count 0.153 1.17 +16.52% per additional variant (RH+nvidia) | |
| format_x_size_infrequent_sklearn -0.062 0.94 ×0.94 vs baseline | |
| prior_same_format_exists 0.040 1.04 ×1.04 when same format already released | |
| days_since_base -0.003 1.00 -0.31% per unit | |
| days_since_prior_variant -0.002 1.00 -0.15% per day since prior variant published | |
| ====================================================================== | |
| Format effectiveness by model size (coefficient = log-multiplier on downloads) | |
| Positive = more downloads than format average, negative = fewer | |
| ====================================================================== | |
| Format <5B 5-15B 15-50B | |
| --------------------------------------------- | |
| fp8 2.42 2.59 2.68 | |
| gguf -0.66 — — | |
| nvfp4 — 3.22 3.41 | |
| w4a16 1.84 2.74 2.39 | |
| w8a16 -1.39 1.92 -0.30 | |
| w8a8 2.17 2.63 1.14 | |
| ====================================================================== | |
| Same table as download multiplier (1.00 = neutral) | |
| ====================================================================== | |
| Format <5B 5-15B 15-50B | |
| --------------------------------------------- | |
| fp8 11.23 13.35 14.54 | |
| gguf 0.52 — — | |
| nvfp4 — 24.93 30.29 | |
| w4a16 6.29 15.53 10.93 | |
| w8a16 0.25 6.79 0.74 | |
| w8a8 8.73 13.84 3.13 | |
| ====================================================================== | |
| Sample counts per format × size | |
| ====================================================================== | |
| size_bucket 15-50B 5-15B <5B | |
| quant_format | |
| deepsparse 0 8 0 | |
| fp8 73 50 30 | |
| gguf 0 2 3 | |
| gptq 0 1 0 | |
| nvfp4 24 6 0 | |
| w4a16 29 25 21 | |
| w8a16 11 10 12 | |
| w8a8 26 22 26 | |
| ====================================================================== | |
| Downloads by first-mover status (RedHatAI) | |
| ====================================================================== | |
| First quant variant for base: n=182, mean=293,267, median=8,064 | |
| Prior variant existed: n=197, mean=138,678, median=7,881 | |
| First of this quant format for base: n=345, mean=208,504, median=7,422 | |
| Same format already existed: n=34, mean=257,657, median=23,134 | |
| ====================================================================== | |
| Downloads by release timing relative to base model (RedHatAI) | |
| ====================================================================== | |
| Within 1 day (0-1d): n=11, mean=207,404, median=324 | |
| Within 1 week (2-7d): n=48, mean=683,608, median=37,746 | |
| Within 1 month (8-30d): n=176, mean=411,720, median=23,498 | |
| After 1 month (31+d): n=203, mean=40,549, median=3,624 |
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| #!/usr/bin/env python3 | |
| """Train a linear model to predict quantized-model downloads from hub, time-to-release, format, and size.""" | |
| import argparse | |
| import json | |
| import os | |
| import re | |
| import sys | |
| import urllib.parse | |
| import urllib.request | |
| from datetime import datetime | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.preprocessing import OneHotEncoder | |
| CSV_PATH = "hf_quant_dataset.csv" | |
| # --------------------------------------------------------------------------- | |
| # Data fetching (shared logic with hf_quant_compare.py) | |
| # --------------------------------------------------------------------------- | |
| QUANT_KEYWORDS = [ | |
| "FP4", "FP8", "GPTQ", "AWQ", "INT4", "INT8", "NVFP4", | |
| "W4A16", "W8A8", "W8A16", "W4A4", "quantized", "GGUF", "AutoFP8", | |
| ] | |
| ORG_PREFIXES = [ | |
| ("qwen", "Qwen"), | |
| ("llama", "meta-llama"), | |
| ("gemma", "google"), | |
| ("diffusiongem","google"), | |
| ("deepseek", "deepseek-ai"), | |
| ("glm", "THUDM"), | |
| ("mistral", "mistralai"), | |
| ("mixtral", "mistralai"), | |
| ("devstral", "mistralai"), | |
| ("phi-", "microsoft"), | |
| ("kimi", "moonshotai"), | |
| ("nemotron", "nvidia"), | |
| ("nvidia-nemotron", "nvidia"), | |
| ("minimax", "MiniMaxAI"), | |
| ("granite", "ibm-granite"), | |
| ("smollm", "HuggingFaceTB"), | |
| ("command", "CohereForAI"), | |
| ("whisper", "openai"), | |
| ("apertus", "ibm-granite"), | |
| ("omnicoder", "microsoft"), | |
| ("trinity", "Snowflake"), | |
| ] | |
| def fetch_models(author): | |
| url = ( | |
| f"https://huggingface.co/api/models?author={author}&limit=10000" | |
| "&expand%5B%5D=downloadsAllTime" | |
| "&expand%5B%5D=downloads" | |
| "&expand%5B%5D=createdAt" | |
| ) | |
| with urllib.request.urlopen(url) as resp: | |
| return json.loads(resp.read()) | |
| def is_quantized(name): | |
| return any(kw.lower() in name.lower() for kw in QUANT_KEYWORDS) | |
| SUFFIXES_RE = re.compile( | |
| r"[-_](" | |
| r"NVFP4A16|NVFP4-FP8-BLOCK|NVFP4-FP8|NVFP4-v\d+|NVFP4-QAD|NVFP4" | |
| r"|FP8-dynamic|FP8-Dynamic|FP8-BLOCK|FP8-block|FP8-Dflash|FP8-KV|FP8" | |
| r"|FP4-QAD|FP4-Eagle\d+" | |
| r"|GPTQ-\d+bit|GPTQ|AutoFP8" | |
| r"|quantized\.w\d+a\d+|quantized-deepsparse" | |
| r"|W4A16-G\d+|W4A16" | |
| r"|GGUF" | |
| r"|ONNX-INT4" | |
| r")$", | |
| re.IGNORECASE, | |
| ) | |
| FIRST_QUANT_RE = re.compile( | |
| r"[-_.](" | |
| r"NVFP4A16|NVFP4|FP8|FP4|GPTQ|AutoFP8|quantized|W4A16|W8A8|W8A16|W4A4|GGUF|ONNX-INT4" | |
| r")(?:[-_.\b]|$)", | |
| re.IGNORECASE, | |
| ) | |
| def extract_base(name): | |
| m = FIRST_QUANT_RE.search(name) | |
| if m: | |
| return name[:m.start()] | |
| return name | |
| def normalize_base(base): | |
| b = base.lower() | |
| b = re.sub(r"^meta-", "", b) | |
| b = b.replace("_", "-") | |
| b = re.sub(r"-it$", "", b) | |
| return b | |
| def extract_quant_format(name): | |
| """Return the quantization format tag from a model name.""" | |
| m = SUFFIXES_RE.search(name) | |
| if not m: | |
| return "unknown" | |
| fmt = m.group(1).lower() | |
| if fmt.startswith("quantized."): | |
| return fmt.replace("quantized.", "") # e.g. "w4a16" | |
| if fmt.startswith("quantized"): | |
| return fmt.replace("quantized-", "") | |
| if "nvfp4" in fmt: | |
| return "nvfp4" | |
| if "fp8" in fmt: | |
| return "fp8" | |
| if "gptq" in fmt: | |
| return "gptq" | |
| if "autofp8" in fmt: | |
| return "fp8" | |
| if "gguf" in fmt: | |
| return "gguf" | |
| if "onnx-int4" in fmt: | |
| return "int4" | |
| return fmt | |
| SIZE_RE = re.compile(r"(\d+(?:\.\d+)?)\s*[Bb]\b") | |
| def extract_model_size_b(name): | |
| """Pull the largest 'NB' parameter count (in billions) from name. Returns float or NaN.""" | |
| matches = SIZE_RE.findall(name) | |
| if not matches: | |
| return float("nan") | |
| return max(float(x) for x in matches) | |
| # --------------------------------------------------------------------------- | |
| # Original-date resolution (with caching) | |
| # --------------------------------------------------------------------------- | |
| def _fetch_date(model_id): | |
| try: | |
| url = f"https://huggingface.co/api/models/{model_id}" | |
| with urllib.request.urlopen(url, timeout=10) as resp: | |
| data = json.loads(resp.read()) | |
| c = data.get("createdAt", "") | |
| return c[:10] if c else None | |
| except Exception: | |
| return None | |
| def _fetch_downloads(model_id): | |
| try: | |
| url = f"https://huggingface.co/api/models/{model_id}?expand%5B%5D=downloadsAllTime" | |
| with urllib.request.urlopen(url, timeout=10) as resp: | |
| data = json.loads(resp.read()) | |
| return data.get("downloadsAllTime", 0) | |
| except Exception: | |
| return None | |
| def _search_date(base_name): | |
| try: | |
| url = f"https://huggingface.co/api/models?search={urllib.parse.quote(base_name)}&limit=20" | |
| with urllib.request.urlopen(url, timeout=10) as resp: | |
| models = json.loads(resp.read()) | |
| best_date, best_id = None, None | |
| for m in models: | |
| n = m["id"].split("/")[-1] | |
| if is_quantized(n): | |
| continue | |
| c = (m.get("createdAt") or "")[:10] | |
| if c and (best_date is None or c < best_date): | |
| best_date = c | |
| best_id = m["id"] | |
| return best_date, best_id | |
| except Exception: | |
| return None, None | |
| def _guess_candidates(norm_base, sample_name): | |
| base_cased = extract_base(sample_name) | |
| base_no_meta = re.sub(r"^Meta-", "", base_cased) | |
| for prefix, org in ORG_PREFIXES: | |
| if norm_base.startswith(prefix): | |
| cands = [f"{org}/{base_cased}", f"{org}/{base_no_meta}"] | |
| if norm_base.startswith("gemma") or norm_base.startswith("diffusiongem"): | |
| cands += [f"{org}/{base_cased}-it", f"{org}/{base_no_meta}-it"] | |
| return cands | |
| return [] | |
| def resolve_original_info(norm_base, sample_name, cache): | |
| """Look up the original model's release date and downloads. Returns (date, downloads).""" | |
| if norm_base in cache: | |
| return cache[norm_base] | |
| for cand in _guess_candidates(norm_base, sample_name): | |
| date = _fetch_date(cand) | |
| if date: | |
| dl = _fetch_downloads(cand) | |
| cache[norm_base] = (date, dl) | |
| return (date, dl) | |
| date, repo_id = _search_date(extract_base(sample_name)) | |
| dl = _fetch_downloads(repo_id) if repo_id else None | |
| cache[norm_base] = (date, dl) | |
| return (date, dl) | |
| # --------------------------------------------------------------------------- | |
| # Build dataset | |
| # --------------------------------------------------------------------------- | |
| def _resolve_bases(df): | |
| """Resolve base-model dates and downloads, marking failures.""" | |
| cache = {} | |
| bases = df["norm_base"].unique() | |
| failed = [] | |
| print(f"\nResolving original release dates for {len(bases)} base models...") | |
| for i, base in enumerate(sorted(bases), 1): | |
| sample = df[df["norm_base"] == base].iloc[0]["name"] | |
| resolve_original_info(base, sample, cache) | |
| date, dl = cache.get(base, (None, None)) | |
| if date is None: | |
| failed.append(base) | |
| sys.stdout.write(f"\r [{i}/{len(bases)}]") | |
| sys.stdout.flush() | |
| print() | |
| if failed: | |
| print(f" WARNING: {len(failed)} base models failed to resolve:") | |
| for b in failed: | |
| print(f" - {b}") | |
| return cache, failed | |
| def _add_derived_columns(df): | |
| """Add all derived columns needed by models.""" | |
| df["log_base_downloads"] = np.log1p(df["base_downloads"].fillna(0).astype(float)) | |
| df["created_dt"] = pd.to_datetime(df["created"]) | |
| df["base_dt"] = pd.to_datetime(df["base_date"]) | |
| df["days_since_base"] = (df["created_dt"] - df["base_dt"]).dt.days | |
| today = pd.Timestamp.now().normalize() | |
| df["days_since_release"] = (today - df["created_dt"]).dt.days | |
| df["year_half"] = df["created_dt"].apply( | |
| lambda d: f"{d.year}H{'1' if d.month <= 6 else '2'}" | |
| ) | |
| # Timing buckets (inclusive) | |
| df["within_1_day"] = ((df["days_since_base"] >= 0) & (df["days_since_base"] <= 1)).astype(int) | |
| df["within_1_week"] = ((df["days_since_base"] >= 0) & (df["days_since_base"] <= 7)).astype(int) | |
| df["within_1_month"] = ((df["days_since_base"] >= 0) & (df["days_since_base"] <= 30)).astype(int) | |
| # Size buckets | |
| def size_bucket(size_b): | |
| if pd.isna(size_b): | |
| return "unknown" | |
| if size_b < 5: | |
| return "<5B" | |
| elif size_b < 15: | |
| return "5-15B" | |
| else: | |
| return "15-50B" # Includes >50B models too | |
| df["size_bucket"] = df["model_size_b"].apply(size_bucket) | |
| df = df.sort_values("created_dt").reset_index(drop=True) | |
| prior_any = [] | |
| prior_same_fmt = [] | |
| total_variants_count = [] | |
| days_since_prior_variant = [] | |
| for _, row in df.iterrows(): | |
| same_base = df[ | |
| (df["norm_base"] == row["norm_base"]) | |
| & (df["created_dt"] < row["created_dt"]) | |
| ] | |
| prior_any.append(1 if len(same_base) > 0 else 0) | |
| same_fmt = same_base[same_base["quant_format"] == row["quant_format"]] | |
| prior_same_fmt.append(1 if len(same_fmt) > 0 else 0) | |
| # Count total variants for this base model across both hubs (at time of release) | |
| all_same_base = df[df["norm_base"] == row["norm_base"]] | |
| total_variants_count.append(len(all_same_base)) | |
| # Calculate days since prior variant was published (0 if first variant) | |
| if len(same_base) > 0: | |
| most_recent_prior = same_base.iloc[-1] # Already sorted by created_dt | |
| days_diff = (row["created_dt"] - most_recent_prior["created_dt"]).days | |
| days_since_prior_variant.append(days_diff) | |
| else: | |
| days_since_prior_variant.append(0) | |
| df["prior_variant_exists"] = prior_any | |
| df["prior_same_format_exists"] = prior_same_fmt | |
| df["total_variants_count"] = total_variants_count | |
| df["days_since_prior_variant"] = days_since_prior_variant | |
| return df | |
| def build_dataset(): | |
| print("Fetching RedHatAI models...") | |
| rh_all = fetch_models("RedHatAI") | |
| print(f" {len(rh_all)} total") | |
| print("Fetching Nvidia models...") | |
| nv_all = fetch_models("nvidia") | |
| print(f" {len(nv_all)} total") | |
| rows = [] | |
| for hub, models in [("RedHatAI", rh_all), ("nvidia", nv_all)]: | |
| for m in models: | |
| name = m["id"].split("/")[-1] | |
| if not is_quantized(name): | |
| continue | |
| rows.append({ | |
| "hub": hub, | |
| "name": name, | |
| "created": (m.get("createdAt") or "")[:10], | |
| "downloads": m.get("downloadsAllTime", m.get("downloads", 0)), | |
| "downloads_30d": m.get("downloads", 0), | |
| "quant_format": extract_quant_format(name), | |
| "model_size_b": extract_model_size_b(name), | |
| "norm_base": normalize_base(extract_base(name)), | |
| }) | |
| df = pd.DataFrame(rows) | |
| df = df[df["created"] != ""] | |
| df = df.dropna(subset=["model_size_b"]) | |
| cache, failed = _resolve_bases(df) | |
| df["base_date"] = df["norm_base"].map(lambda b: cache.get(b, (None, None))[0]) | |
| df["base_downloads"] = df["norm_base"].map(lambda b: cache.get(b, (None, None))[1]) | |
| # Keep rows with failed resolution in CSV (base_date="FAILED") but exclude from training | |
| df.loc[df["base_date"].isna(), "base_date"] = "FAILED" | |
| df.to_csv(CSV_PATH, index=False) | |
| n_failed = (df["base_date"] == "FAILED").sum() | |
| print(f" Saved {len(df)} rows to {CSV_PATH} ({n_failed} with FAILED base_date)") | |
| df = df[df["base_date"] != "FAILED"] | |
| return _add_derived_columns(df) | |
| def retry_failed(): | |
| """Load CSV, re-resolve only FAILED base models, save back.""" | |
| if not os.path.exists(CSV_PATH): | |
| print(f"No {CSV_PATH} found. Run without --retry first.") | |
| sys.exit(1) | |
| df = pd.read_csv(CSV_PATH) | |
| failed_mask = df["base_date"] == "FAILED" | |
| failed_bases = df.loc[failed_mask, "norm_base"].unique() | |
| if len(failed_bases) == 0: | |
| print("No FAILED rows to retry.") | |
| else: | |
| print(f"Retrying {len(failed_bases)} failed base models...") | |
| cache = {} | |
| for i, base in enumerate(sorted(failed_bases), 1): | |
| sample = df[df["norm_base"] == base].iloc[0]["name"] | |
| resolve_original_info(base, sample, cache) | |
| date, dl = cache.get(base, (None, None)) | |
| status = date if date else "STILL FAILED" | |
| sys.stdout.write(f"\r [{i}/{len(failed_bases)}] {base}: {status} ") | |
| sys.stdout.flush() | |
| print() | |
| fixed = 0 | |
| for base, (date, dl) in cache.items(): | |
| if date: | |
| mask = df["norm_base"] == base | |
| df.loc[mask, "base_date"] = date | |
| df.loc[mask, "base_downloads"] = dl | |
| fixed += mask.sum() | |
| still_failed = (df["base_date"] == "FAILED").sum() | |
| print(f" Fixed {fixed} rows, {still_failed} still FAILED") | |
| df.to_csv(CSV_PATH, index=False) | |
| print(f" Saved to {CSV_PATH}") | |
| df = df[df["base_date"] != "FAILED"] | |
| return _add_derived_columns(df) | |
| # --------------------------------------------------------------------------- | |
| # Train and report | |
| # --------------------------------------------------------------------------- | |
| def train_and_report(df): | |
| features = df[["hub", "days_since_base", "days_since_release", "quant_format", "model_size_b", "log_base_downloads"]].copy() | |
| y = df["downloads"].values.astype(float) | |
| # Log-transform downloads (avoids heavy right skew dominating the fit) | |
| y_log = np.log1p(y) | |
| # One-hot encode categoricals | |
| cat_cols = ["hub", "quant_format"] | |
| enc = OneHotEncoder(sparse_output=False, drop="first", handle_unknown="infrequent_if_exist", min_frequency=5) | |
| cat_encoded = enc.fit_transform(features[cat_cols]) | |
| cat_names = enc.get_feature_names_out(cat_cols) | |
| num_cols = ["days_since_base", "days_since_release", "model_size_b", "log_base_downloads"] | |
| num_data = features[num_cols].values.astype(float) | |
| X = np.hstack([num_data, cat_encoded]) | |
| col_names = list(num_cols) + list(cat_names) | |
| model = LinearRegression() | |
| model.fit(X, y_log) | |
| r2 = model.score(X, y_log) | |
| print(f"\n{'=' * 70}") | |
| print(f" Model 1: All models — hub, format, size, base popularity") | |
| print(f" R² = {r2:.4f} (n = {len(y)})") | |
| print(f"{'=' * 70}") | |
| print(f"\n{'Feature':<40} {'Coefficient':>12} Interpretation") | |
| print("-" * 80) | |
| print(f"{'(intercept)':<40} {model.intercept_:>12.4f} baseline log-downloads") | |
| order = np.argsort(np.abs(model.coef_))[::-1] | |
| for i in order: | |
| coef = model.coef_[i] | |
| name = col_names[i] | |
| pct = (np.exp(coef) - 1) * 100 | |
| if name in num_cols: | |
| interp = f"{pct:+.2f}% downloads per unit increase" | |
| else: | |
| interp = f"{pct:+.1f}% vs baseline category" | |
| print(f"{name:<40} {coef:>12.4f} {interp}") | |
| # Summary stats per format | |
| print(f"\n{'=' * 70}") | |
| print(f" Mean downloads by quant format") | |
| print(f"{'=' * 70}") | |
| fmt_stats = df.groupby("quant_format")["downloads"].agg(["count", "mean", "median"]) | |
| fmt_stats = fmt_stats.sort_values("mean", ascending=False) | |
| fmt_stats["mean"] = fmt_stats["mean"].astype(int) | |
| fmt_stats["median"] = fmt_stats["median"].astype(int) | |
| print(fmt_stats.to_string()) | |
| return model, enc, col_names | |
| def train_format_trend_model(df): | |
| """Model 2: RedHatAI only, with format × year_half interaction.""" | |
| df2 = df[df["hub"] == "RedHatAI"].copy() | |
| df2["format_x_period"] = df2["quant_format"] + ":" + df2["year_half"] | |
| features = df2[["days_since_base", "model_size_b", "log_base_downloads", "format_x_period", "within_1_day", "within_1_week", "within_1_month"]].copy() | |
| y = df2["downloads"].values.astype(float) | |
| y_log = np.log1p(y) | |
| cat_cols = ["format_x_period"] | |
| enc = OneHotEncoder(sparse_output=False, drop="first", handle_unknown="infrequent_if_exist", min_frequency=3) | |
| cat_encoded = enc.fit_transform(features[cat_cols]) | |
| cat_names = enc.get_feature_names_out(cat_cols) | |
| num_cols = ["days_since_base", "model_size_b", "log_base_downloads", "within_1_day", "within_1_week", "within_1_month"] | |
| num_data = features[num_cols].values.astype(float) | |
| X = np.hstack([num_data, cat_encoded]) | |
| col_names = list(num_cols) + list(cat_names) | |
| model = LinearRegression() | |
| model.fit(X, y_log) | |
| r2 = model.score(X, y_log) | |
| print(f"\n{'=' * 70}") | |
| print(f" Model 2: RedHatAI only — format × period interaction") | |
| print(f" R² = {r2:.4f} (n = {len(y)})") | |
| print(f"{'=' * 70}") | |
| print(f"\n{'Feature':<50} {'Coef':>8} {'Multiplier':>10} Interpretation") | |
| print("-" * 100) | |
| print(f"{'(intercept)':<50} {model.intercept_:>8.3f} {'':>10} baseline") | |
| order = np.argsort(np.abs(model.coef_))[::-1] | |
| for i in order: | |
| coef = model.coef_[i] | |
| name = col_names[i] | |
| mult = np.exp(coef) | |
| if name in num_cols: | |
| if name == "within_1_day": | |
| interp = f"{'×'}{mult:.2f} when released within 1 day of base" | |
| elif name == "within_1_week": | |
| interp = f"{'×'}{mult:.2f} when released within 1 week of base" | |
| elif name == "within_1_month": | |
| interp = f"{'×'}{mult:.2f} when released within 1 month of base" | |
| else: | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per unit" | |
| else: | |
| interp = f"{'×'}{mult:.2f} vs baseline" | |
| print(f"{name:<50} {coef:>8.3f} {mult:>10.2f} {interp}") | |
| # Extract just the format×period interactions and pivot into a readable table | |
| interactions = {} | |
| for i, name in enumerate(col_names): | |
| if name.startswith("format_x_period_"): | |
| tag = name.replace("format_x_period_", "") | |
| if ":" in tag: | |
| fmt, period = tag.rsplit(":", 1) | |
| interactions.setdefault(fmt, {})[period] = model.coef_[i] | |
| if interactions: | |
| periods = sorted({p for fmt_dict in interactions.values() for p in fmt_dict}) | |
| formats = sorted(interactions.keys()) | |
| print(f"\n{'=' * 70}") | |
| print(f" Format interest over time (coefficient = log-multiplier on downloads)") | |
| print(f" Positive = more downloads than format average, negative = fewer") | |
| print(f"{'=' * 70}") | |
| header = f"{'Format':<15}" + "".join(f"{p:>10}" for p in periods) | |
| print(header) | |
| print("-" * len(header)) | |
| for fmt in formats: | |
| vals = interactions[fmt] | |
| row = f"{fmt:<15}" | |
| for p in periods: | |
| if p in vals: | |
| row += f"{vals[p]:>10.2f}" | |
| else: | |
| row += f"{'—':>10}" | |
| print(row) | |
| # Also show as multiplier (more intuitive) | |
| print(f"\n{'=' * 70}") | |
| print(f" Same table as download multiplier (1.00 = neutral)") | |
| print(f"{'=' * 70}") | |
| print(header) | |
| print("-" * len(header)) | |
| for fmt in formats: | |
| vals = interactions[fmt] | |
| row = f"{fmt:<15}" | |
| for p in periods: | |
| if p in vals: | |
| row += f"{np.exp(vals[p]):>10.2f}" | |
| else: | |
| row += f"{'—':>10}" | |
| print(row) | |
| # Show sample counts per format×period | |
| print(f"\n{'=' * 70}") | |
| print(f" Sample counts per format × period") | |
| print(f"{'=' * 70}") | |
| ct = pd.crosstab(df2["quant_format"], df2["year_half"]) | |
| print(ct.to_string()) | |
| return model, enc, col_names | |
| def train_first_mover_model(df): | |
| """Model 3: RedHatAI only, prior variant (no interactions, no timing buckets).""" | |
| df3 = df[df["hub"] == "RedHatAI"].copy() | |
| features = df3[[ | |
| "days_since_base", | |
| "model_size_b", "quant_format", "prior_variant_exists", | |
| "prior_same_format_exists", "log_base_downloads", | |
| "total_variants_count", "days_since_prior_variant", | |
| ]].copy() | |
| y = df3["downloads"].values.astype(float) | |
| y_log = np.log1p(y) | |
| cat_cols = ["quant_format"] | |
| enc = OneHotEncoder(sparse_output=False, drop="first", handle_unknown="infrequent_if_exist", min_frequency=3) | |
| cat_encoded = enc.fit_transform(features[cat_cols]) | |
| cat_names = enc.get_feature_names_out(cat_cols) | |
| num_cols = ["days_since_base", "model_size_b", "prior_variant_exists", "prior_same_format_exists", "log_base_downloads", "total_variants_count", "days_since_prior_variant"] | |
| num_data = features[num_cols].values.astype(float) | |
| X = np.hstack([num_data, cat_encoded]) | |
| col_names = list(num_cols) + list(cat_names) | |
| model = LinearRegression() | |
| model.fit(X, y_log) | |
| r2 = model.score(X, y_log) | |
| print(f"\n{'=' * 70}") | |
| print(f" Model 3: RedHatAI only — prior variant exists (no interactions, no timing)") | |
| print(f" R² = {r2:.4f} (n = {len(y)})") | |
| print(f"{'=' * 70}") | |
| print(f"\n{'Feature':<50} {'Coef':>8} {'Multiplier':>10} Interpretation") | |
| print("-" * 100) | |
| print(f"{'(intercept)':<50} {model.intercept_:>8.3f} {'':>10} baseline") | |
| order = np.argsort(np.abs(model.coef_))[::-1] | |
| for i in order: | |
| coef = model.coef_[i] | |
| name = col_names[i] | |
| mult = np.exp(coef) | |
| if name in num_cols: | |
| if name == "prior_variant_exists": | |
| interp = f"{'×'}{mult:.2f} when any prior variant exists" | |
| elif name == "prior_same_format_exists": | |
| interp = f"{'×'}{mult:.2f} when same format already released" | |
| elif name == "total_variants_count": | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per additional variant (RH+nvidia)" | |
| elif name == "days_since_prior_variant": | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per day since prior variant published" | |
| else: | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per unit" | |
| else: | |
| interp = f"{'×'}{mult:.2f} vs baseline" | |
| print(f"{name:<50} {coef:>8.3f} {mult:>10.2f} {interp}") | |
| # Show breakdowns | |
| print(f"\n{'=' * 70}") | |
| print(f" Downloads by first-mover status (RedHatAI)") | |
| print(f"{'=' * 70}") | |
| for val in [0, 1]: | |
| sub = df3[df3["prior_variant_exists"] == val] | |
| label = "First quant variant for base" if val == 0 else "Prior variant existed" | |
| print(f" {label}: n={len(sub)}, mean={sub['downloads'].mean():,.0f}, median={sub['downloads'].median():,.0f}") | |
| print() | |
| for val in [0, 1]: | |
| sub = df3[df3["prior_same_format_exists"] == val] | |
| label = "First of this quant format for base" if val == 0 else "Same format already existed" | |
| print(f" {label}: n={len(sub)}, mean={sub['downloads'].mean():,.0f}, median={sub['downloads'].median():,.0f}") | |
| return model, enc, col_names | |
| def train_format_size_model(df): | |
| """Model 4: RedHatAI only, format × size + prior variant exists + timing buckets.""" | |
| df4 = df[df["hub"] == "RedHatAI"].copy() | |
| df4["format_x_size"] = df4["quant_format"] + ":" + df4["size_bucket"] | |
| features = df4[[ | |
| "days_since_base", | |
| "format_x_size", "prior_variant_exists", | |
| "prior_same_format_exists", "log_base_downloads", | |
| "within_1_day", "within_1_week", "within_1_month", | |
| "total_variants_count", "days_since_prior_variant", | |
| ]].copy() | |
| y = df4["downloads"].values.astype(float) | |
| y_log = np.log1p(y) | |
| cat_cols = ["format_x_size"] | |
| enc = OneHotEncoder(sparse_output=False, drop="first", handle_unknown="infrequent_if_exist", min_frequency=3) | |
| cat_encoded = enc.fit_transform(features[cat_cols]) | |
| cat_names = enc.get_feature_names_out(cat_cols) | |
| num_cols = ["days_since_base", "prior_variant_exists", "prior_same_format_exists", "log_base_downloads", "within_1_day", "within_1_week", "within_1_month", "total_variants_count", "days_since_prior_variant"] | |
| num_data = features[num_cols].values.astype(float) | |
| X = np.hstack([num_data, cat_encoded]) | |
| col_names = list(num_cols) + list(cat_names) | |
| model = LinearRegression() | |
| model.fit(X, y_log) | |
| r2 = model.score(X, y_log) | |
| print(f"\n{'=' * 70}") | |
| print(f" Model 4: RedHatAI only — format × size + prior variant exists + timing") | |
| print(f" R² = {r2:.4f} (n = {len(y)})") | |
| print(f"{'=' * 70}") | |
| print(f"\n{'Feature':<50} {'Coef':>8} {'Multiplier':>10} Interpretation") | |
| print("-" * 100) | |
| print(f"{'(intercept)':<50} {model.intercept_:>8.3f} {'':>10} baseline") | |
| order = np.argsort(np.abs(model.coef_))[::-1] | |
| for i in order: | |
| coef = model.coef_[i] | |
| name = col_names[i] | |
| mult = np.exp(coef) | |
| if name in num_cols: | |
| if name == "prior_variant_exists": | |
| interp = f"{'×'}{mult:.2f} when any prior variant exists" | |
| elif name == "prior_same_format_exists": | |
| interp = f"{'×'}{mult:.2f} when same format already released" | |
| elif name == "within_1_day": | |
| interp = f"{'×'}{mult:.2f} when released within 1 day of base" | |
| elif name == "within_1_week": | |
| interp = f"{'×'}{mult:.2f} when released within 1 week of base" | |
| elif name == "within_1_month": | |
| interp = f"{'×'}{mult:.2f} when released within 1 month of base" | |
| elif name == "total_variants_count": | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per additional variant (RH+nvidia)" | |
| elif name == "days_since_prior_variant": | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per day since prior variant published" | |
| else: | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per unit" | |
| else: | |
| interp = f"{'×'}{mult:.2f} vs baseline" | |
| print(f"{name:<50} {coef:>8.3f} {mult:>10.2f} {interp}") | |
| # Extract just the format×size interactions and pivot into a readable table | |
| interactions = {} | |
| for i, name in enumerate(col_names): | |
| if name.startswith("format_x_size_"): | |
| tag = name.replace("format_x_size_", "") | |
| if ":" in tag: | |
| fmt, size = tag.rsplit(":", 1) | |
| interactions.setdefault(fmt, {})[size] = model.coef_[i] | |
| if interactions: | |
| sizes = ["<5B", "5-15B", "15-50B"] # Fixed order | |
| formats = sorted(interactions.keys()) | |
| print(f"\n{'=' * 70}") | |
| print(f" Format effectiveness by model size (coefficient = log-multiplier on downloads)") | |
| print(f" Positive = more downloads than format average, negative = fewer") | |
| print(f"{'=' * 70}") | |
| header = f"{'Format':<15}" + "".join(f"{s:>10}" for s in sizes) | |
| print(header) | |
| print("-" * len(header)) | |
| for fmt in formats: | |
| vals = interactions[fmt] | |
| row = f"{fmt:<15}" | |
| for s in sizes: | |
| if s in vals: | |
| row += f"{vals[s]:>10.2f}" | |
| else: | |
| row += f"{'—':>10}" | |
| print(row) | |
| # Also show as multiplier (more intuitive) | |
| print(f"\n{'=' * 70}") | |
| print(f" Same table as download multiplier (1.00 = neutral)") | |
| print(f"{'=' * 70}") | |
| print(header) | |
| print("-" * len(header)) | |
| for fmt in formats: | |
| vals = interactions[fmt] | |
| row = f"{fmt:<15}" | |
| for s in sizes: | |
| if s in vals: | |
| row += f"{np.exp(vals[s]):>10.2f}" | |
| else: | |
| row += f"{'—':>10}" | |
| print(row) | |
| # Show sample counts per format×size | |
| print(f"\n{'=' * 70}") | |
| print(f" Sample counts per format × size") | |
| print(f"{'=' * 70}") | |
| ct = pd.crosstab(df4["quant_format"], df4["size_bucket"]) | |
| print(ct.to_string()) | |
| # Show breakdowns | |
| print(f"\n{'=' * 70}") | |
| print(f" Downloads by first-mover status (RedHatAI)") | |
| print(f"{'=' * 70}") | |
| for val in [0, 1]: | |
| sub = df4[df4["prior_variant_exists"] == val] | |
| label = "First quant variant for base" if val == 0 else "Prior variant existed" | |
| print(f" {label}: n={len(sub)}, mean={sub['downloads'].mean():,.0f}, median={sub['downloads'].median():,.0f}") | |
| print() | |
| for val in [0, 1]: | |
| sub = df4[df4["prior_same_format_exists"] == val] | |
| label = "First of this quant format for base" if val == 0 else "Same format already existed" | |
| print(f" {label}: n={len(sub)}, mean={sub['downloads'].mean():,.0f}, median={sub['downloads'].median():,.0f}") | |
| print(f"\n{'=' * 70}") | |
| print(f" Downloads by release timing relative to base model (RedHatAI)") | |
| print(f"{'=' * 70}") | |
| timing_data = [ | |
| ("Within 1 day (0-1d)", df4["within_1_day"] == 1), | |
| ("Within 1 week (2-7d)", df4["within_1_week"] == 1), | |
| ("Within 1 month (8-30d)", df4["within_1_month"] == 1), | |
| ("After 1 month (31+d)", (df4["within_1_day"] == 0) & (df4["within_1_week"] == 0) & (df4["within_1_month"] == 0)), | |
| ] | |
| for label, mask in timing_data: | |
| sub = df4[mask] | |
| if len(sub) > 0: | |
| print(f" {label}: n={len(sub)}, mean={sub['downloads'].mean():,.0f}, median={sub['downloads'].median():,.0f}") | |
| return model, enc, col_names | |
| def train_overlap_model(df): | |
| """Model 5: RedHatAI only, limited to (base, format) pairs where both RH and nvidia released within 1 week.""" | |
| # Find overlapping (norm_base, quant_format) pairs where both released within 1 week of each other | |
| overlap_pairs = set() | |
| for (norm_base, quant_format) in df[["norm_base", "quant_format"]].drop_duplicates().values: | |
| rh_models = df[(df["hub"] == "RedHatAI") & (df["norm_base"] == norm_base) & (df["quant_format"] == quant_format)] | |
| nv_models = df[(df["hub"] == "nvidia") & (df["norm_base"] == norm_base) & (df["quant_format"] == quant_format)] | |
| if len(rh_models) > 0 and len(nv_models) > 0: | |
| # Get release dates | |
| rh_dates = pd.to_datetime(rh_models["created"]) | |
| nv_dates = pd.to_datetime(nv_models["created"]) | |
| # Check if any pair released within 14 days of each other | |
| for rh_date in rh_dates: | |
| for nv_date in nv_dates: | |
| if abs((rh_date - nv_date).days) <= 14: | |
| overlap_pairs.add((norm_base, quant_format)) | |
| break | |
| if (norm_base, quant_format) in overlap_pairs: | |
| break | |
| # Filter to RedHatAI models where the (base, format) pair exists in both within 1 week | |
| df5 = df[df["hub"] == "RedHatAI"].copy() | |
| df5["pair"] = df5[["norm_base", "quant_format"]].apply(tuple, axis=1) | |
| df5 = df5[df5["pair"].isin(overlap_pairs)].copy() | |
| print(f"\n Model 5 uses {len(df5)} RedHatAI models (out of {len(df[df['hub'] == 'RedHatAI'])} total)") | |
| print(f" These are models where both RedHatAI and nvidia released the same (base, format) within 2 weeks") | |
| features = df5[[ | |
| "days_since_base", | |
| "model_size_b", "quant_format", "prior_same_format_exists", "log_base_downloads", | |
| ]].copy() | |
| y = df5["downloads"].values.astype(float) | |
| y_log = np.log1p(y) | |
| cat_cols = ["quant_format"] | |
| enc = OneHotEncoder(sparse_output=False, drop="first", handle_unknown="infrequent_if_exist", min_frequency=3) | |
| cat_encoded = enc.fit_transform(features[cat_cols]) | |
| cat_names = enc.get_feature_names_out(cat_cols) | |
| num_cols = ["days_since_base", "model_size_b", "prior_same_format_exists", "log_base_downloads"] | |
| num_data = features[num_cols].values.astype(float) | |
| X = np.hstack([num_data, cat_encoded]) | |
| col_names = list(num_cols) + list(cat_names) | |
| model = LinearRegression() | |
| model.fit(X, y_log) | |
| r2 = model.score(X, y_log) | |
| print(f"\n{'=' * 70}") | |
| print(f" Model 5: RedHatAI only — both RH+nvidia released within 2 weeks") | |
| print(f" R² = {r2:.4f} (n = {len(y)})") | |
| print(f"{'=' * 70}") | |
| print(f"\n{'Feature':<50} {'Coef':>8} {'Multiplier':>10} Interpretation") | |
| print("-" * 100) | |
| print(f"{'(intercept)':<50} {model.intercept_:>8.3f} {'':>10} baseline") | |
| order = np.argsort(np.abs(model.coef_))[::-1] | |
| for i in order: | |
| coef = model.coef_[i] | |
| name = col_names[i] | |
| mult = np.exp(coef) | |
| if name in num_cols: | |
| if name == "prior_same_format_exists": | |
| interp = f"{'×'}{mult:.2f} when same format already released" | |
| else: | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per unit" | |
| else: | |
| interp = f"{'×'}{mult:.2f} vs baseline" | |
| print(f"{name:<50} {coef:>8.3f} {mult:>10.2f} {interp}") | |
| # Show breakdowns | |
| print(f"\n{'=' * 70}") | |
| print(f" Downloads by first-of-format status (RedHatAI, overlap only)") | |
| print(f"{'=' * 70}") | |
| for val in [0, 1]: | |
| sub = df5[df5["prior_same_format_exists"] == val] | |
| label = "First of this quant format for base" if val == 0 else "Same format already existed" | |
| print(f" {label}: n={len(sub)}, mean={sub['downloads'].mean():,.0f}, median={sub['downloads'].median():,.0f}") | |
| return model, enc, col_names | |
| def train_granular_timing_model(df): | |
| """Model 6: RedHatAI only, granular timing of when nvidia released same format.""" | |
| # Find overlapping (norm_base, quant_format) pairs (no timing restriction) | |
| rh_pairs = set(df[df["hub"] == "RedHatAI"][["norm_base", "quant_format"]].apply(tuple, axis=1)) | |
| nv_pairs = set(df[df["hub"] == "nvidia"][["norm_base", "quant_format"]].apply(tuple, axis=1)) | |
| overlap_pairs = rh_pairs & nv_pairs | |
| # Filter to RedHatAI models where the (base, format) pair exists in both | |
| df6 = df[df["hub"] == "RedHatAI"].copy() | |
| df6["pair"] = df6[["norm_base", "quant_format"]].apply(tuple, axis=1) | |
| df6 = df6[df6["pair"].isin(overlap_pairs)].copy() | |
| # For each RedHat model, determine timing relative to base and find nvidia timing | |
| def get_timing_bucket(days): | |
| """Convert days_since_base to timing bucket.""" | |
| if days <= 1: | |
| return "1day" | |
| elif days <= 3: | |
| return "3days" | |
| elif days <= 7: | |
| return "1week" | |
| elif days <= 14: | |
| return "2weeks" | |
| elif days <= 30: | |
| return "1month" | |
| else: | |
| return "after_1month" | |
| rh_nv_interaction = [] | |
| for _, rh_row in df6.iterrows(): | |
| rh_timing = get_timing_bucket(rh_row["days_since_base"]) | |
| nv_models = df[(df["hub"] == "nvidia") & | |
| (df["norm_base"] == rh_row["norm_base"]) & | |
| (df["quant_format"] == rh_row["quant_format"])] | |
| if len(nv_models) > 0: | |
| # Get nvidia's days_since_base (use earliest if multiple) | |
| nv_days_since_base = nv_models["days_since_base"].min() | |
| nv_timing = get_timing_bucket(nv_days_since_base) | |
| interaction = f"rh_{rh_timing}_x_nv_{nv_timing}" | |
| else: | |
| interaction = "no_nv_release" # Shouldn't happen due to filter | |
| rh_nv_interaction.append(interaction) | |
| df6["rh_nv_timing_interaction"] = rh_nv_interaction | |
| print(f"\n Model 6 uses {len(df6)} RedHatAI models (out of {len(df[df['hub'] == 'RedHatAI'])} total)") | |
| print(f" Limited to models where both RedHat and nvidia released the same (base, format)") | |
| print(f"\n RH-NV timing interaction breakdown:") | |
| print(df6["rh_nv_timing_interaction"].value_counts().sort_index()) | |
| features = df6[[ | |
| "model_size_b", "quant_format", "log_base_downloads", "rh_nv_timing_interaction", | |
| ]].copy() | |
| y = df6["downloads"].values.astype(float) | |
| y_log = np.log1p(y) | |
| cat_cols = ["quant_format", "rh_nv_timing_interaction"] | |
| enc = OneHotEncoder(sparse_output=False, drop="first", handle_unknown="infrequent_if_exist", min_frequency=1) | |
| cat_encoded = enc.fit_transform(features[cat_cols]) | |
| cat_names = enc.get_feature_names_out(cat_cols) | |
| num_cols = ["model_size_b", "log_base_downloads"] | |
| num_data = features[num_cols].values.astype(float) | |
| X = np.hstack([num_data, cat_encoded]) | |
| col_names = list(num_cols) + list(cat_names) | |
| model = LinearRegression() | |
| model.fit(X, y_log) | |
| r2 = model.score(X, y_log) | |
| print(f"\n{'=' * 70}") | |
| print(f" Model 6: RedHatAI only — granular timing relative to nvidia release") | |
| print(f" R² = {r2:.4f} (n = {len(y)})") | |
| print(f"{'=' * 70}") | |
| print(f"\n{'Feature':<50} {'Coef':>8} {'Multiplier':>10} Interpretation") | |
| print("-" * 100) | |
| print(f"{'(intercept)':<50} {model.intercept_:>8.3f} {'':>10} baseline") | |
| order = np.argsort(np.abs(model.coef_))[::-1] | |
| for i in order: | |
| coef = model.coef_[i] | |
| name = col_names[i] | |
| mult = np.exp(coef) | |
| if name in num_cols: | |
| pct = (mult - 1) * 100 | |
| interp = f"{pct:+.2f}% per unit" | |
| else: | |
| interp = f"{'×'}{mult:.2f} vs baseline" | |
| print(f"{name:<50} {coef:>8.3f} {mult:>10.2f} {interp}") | |
| # Extract RH-NV timing interactions and pivot into a readable table | |
| interactions = {} | |
| for i, name in enumerate(col_names): | |
| if name.startswith("rh_nv_timing_interaction_"): | |
| tag = name.replace("rh_nv_timing_interaction_", "") | |
| if "_x_nv_" in tag: | |
| parts = tag.split("_x_nv_") | |
| rh_timing = parts[0].replace("rh_", "") | |
| nv_timing = parts[1] | |
| interactions.setdefault(rh_timing, {})[nv_timing] = model.coef_[i] | |
| if interactions: | |
| timing_order = ["1day", "3days", "1week", "2weeks", "1month", "after_1month"] | |
| rh_timings = [t for t in timing_order if t in interactions] | |
| nv_timings = timing_order | |
| print(f"\n{'=' * 90}") | |
| print(f" RH vs NV timing interactions (coefficient = log-multiplier on downloads)") | |
| print(f"{'=' * 90}") | |
| header = f"{'RH timing':<15}" + "".join(f"{'nv_'+nv:>12}" for nv in nv_timings) | |
| print(header) | |
| print("-" * len(header)) | |
| for rh_t in rh_timings: | |
| vals = interactions[rh_t] | |
| row = f"{rh_t:<15}" | |
| for nv_t in nv_timings: | |
| if nv_t in vals: | |
| row += f"{vals[nv_t]:>12.2f}" | |
| else: | |
| row += f"{'—':>12}" | |
| print(row) | |
| # Also show as multiplier | |
| print(f"\n{'=' * 90}") | |
| print(f" Same table as download multiplier (1.00 = neutral)") | |
| print(f"{'=' * 90}") | |
| print(header) | |
| print("-" * len(header)) | |
| for rh_t in rh_timings: | |
| vals = interactions[rh_t] | |
| row = f"{rh_t:<15}" | |
| for nv_t in nv_timings: | |
| if nv_t in vals: | |
| row += f"{np.exp(vals[nv_t]):>12.2f}" | |
| else: | |
| row += f"{'—':>12}" | |
| print(row) | |
| return model, enc, col_names | |
| def main(): | |
| parser = argparse.ArgumentParser(description="Train download prediction models") | |
| parser.add_argument("--retry", action="store_true", | |
| help="Retry fetching only FAILED rows from CSV") | |
| parser.add_argument("--fetch-all", action="store_true", | |
| help="Re-fetch everything from HF API") | |
| args = parser.parse_args() | |
| if args.fetch_all: | |
| df = build_dataset() | |
| elif args.retry: | |
| df = retry_failed() | |
| elif os.path.exists(CSV_PATH): | |
| # Default: use CSV without fetching | |
| print(f"Loading data from {CSV_PATH} (use --retry for failed rows, --fetch-all to re-fetch everything)") | |
| df = pd.read_csv(CSV_PATH) | |
| df = df[df["base_date"] != "FAILED"] | |
| df = _add_derived_columns(df) | |
| else: | |
| # No CSV exists, fetch everything | |
| df = build_dataset() | |
| print(f"\nDataset: {len(df)} quantized models with known base date and model size") | |
| print(f" Hubs: {df['hub'].value_counts().to_dict()}") | |
| print(f" Formats: {df['quant_format'].value_counts().to_dict()}") | |
| print(f" Prior variant exists: {df['prior_variant_exists'].value_counts().to_dict()}") | |
| train_and_report(df) | |
| train_format_trend_model(df) | |
| train_first_mover_model(df) | |
| train_format_size_model(df) | |
| train_overlap_model(df) | |
| train_granular_timing_model(df) | |
| if __name__ == "__main__": | |
| main() |
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| Loading data from hf_quant_dataset.csv... | |
| RedHatAI quantized: 379 | |
| Nvidia quantized: 73 | |
| Download threshold: >100.0K combined | |
| ============================================================ | |
| EXACT MATCHES (16 models, >100.0K combined) [delta = RedHatAI minus Nvidia] | |
| ============================================================ | |
| Model Name | Base Date | RH Date | RH DLs | NV Date | NV DLs | Date Delta | DL Delta | |
| ----------------------------------------|------------|------------|--------|------------|--------|------------|--------- | |
| diffusiongemma-26B-A4B-it-NVFP4 | 2026-06-09 | 2026-06-09 | 606.1K | 2026-06-10 | 1.7M | -1d | -1.1M | |
| NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 | 2026-05-26 | 2026-06-09 | 48 | 2026-06-03 | 527.3K | +6d | -527.3K | |
| Qwen3.6-35B-A3B-NVFP4 | 2026-04-15 | 2026-04-17 | 6.5M | 2026-05-27 | 8.5M | -40d | -2.0M | |
| Qwen3.5-122B-A10B-NVFP4 | 2026-02-24 | 2026-03-17 | 569.6K | 2026-05-13 | 93.9K | -57d | +475.7K | |
| NVIDIA-Nemotron-3-Super-120B-A12B-FP8 | 2026-03-06 | 2026-03-26 | 5.1K | 2026-03-10 | 2.2M | +16d | -2.2M | |
| NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | 2026-03-06 | 2026-03-27 | 11.3K | 2026-03-10 | 5.5M | +17d | -5.5M | |
| Qwen3-Next-80B-A3B-Thinking-NVFP4 | 2025-09-09 | 2025-12-24 | 1.7K | 2025-12-11 | 210.3K | +13d | -208.6K | |
| Qwen3-Next-80B-A3B-Instruct-NVFP4 | 2025-09-09 | 2025-12-12 | 79.1K | 2025-12-09 | 218.1K | +3d | -139.0K | |
| NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 | 2025-12-03 | 2025-12-23 | 9.2K | 2025-12-06 | 5.9M | +17d | -5.9M | |
| Qwen3-14B-NVFP4 | 2025-04-27 | 2025-10-23 | 75.2K | 2025-09-09 | 549.5K | +44d | -474.3K | |
| Qwen3-8B-NVFP4 | 2025-04-27 | 2025-10-23 | 27.8K | 2025-09-09 | 444.6K | +44d | -416.8K | |
| Llama-3.1-8B-Instruct-NVFP4 | 2024-07-18 | 2025-10-23 | 128.1K | 2025-09-05 | 1.0M | +48d | -903.7K | |
| Qwen3-32B-NVFP4 | 2025-04-27 | 2025-06-27 | 139.9K | 2025-09-09 | 627.2K | -74d | -487.3K | |
| Qwen3-30B-A3B-NVFP4 | 2025-04-27 | 2025-06-17 | 190.1K | 2025-07-08 | 705.0K | -21d | -514.9K | |
| Llama-4-Scout-17B-16E-Instruct-NVFP4 | 2025-04-02 | 2025-07-08 | 20.5K | 2025-04-14 | 584.4K | +85d | -563.9K | |
| Llama-3.3-70B-Instruct-NVFP4 | 2024-11-26 | 2025-08-05 | 26.5K | 2025-01-16 | 588.7K | +201d | -562.2K | |
| ============================================================ | |
| FUZZY MATCHES — same base model, different quant (28 base models, >100.0K combined) [delta = RH minus NV] | |
| ============================================================ | |
| Base Model | Full Name | Date | Downloads | Date Delta | DL Delta | Variants | | |
| -----------------------------------|--------------------------------------------------------------------------------|------------|-----------|------------|----------|-----------|---- | |
| diffusiongemma-26b-a4b | [base: diffusiongemma-26b-a4b] | 2026-06-09 | | | | | | |
| | RedHatAI/diffusiongemma-26B-A4B-it-FP8-dynamic | 2026-06-09 | 284.5K | | | | | |
| | RedHatAI/diffusiongemma-26B-A4B-it-NVFP4 | 2026-06-09 | 606.1K | | | | | |
| | nvidia/diffusiongemma-26B-A4B-it-NVFP4 | 2026-06-10 | 1.7M | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -1d | -796.9K | RH:2 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| nvidia-nemotron-3-ultra-550b-a55b | [base: nvidia-nemotron-3-ultra-550b-a55b] | 2026-05-26 | | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block | 2026-06-04 | 7.9K | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-dynamic | 2026-06-04 | 14.8K | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 | 2026-06-09 | 48 | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 | 2026-06-04 | 2.4K | | | | | |
| | nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 | 2026-06-03 | 527.3K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +1d | -502.2K | RH:4 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3.6-27b | [base: qwen3.6-27b] | 2026-04-21 | | | | | | |
| | RedHatAI/Qwen3.6-27B-FP8 | 2026-05-01 | 55.0K | | | | | |
| | nvidia/Qwen3.6-27B-NVFP4 | 2026-06-22 | 538.7K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -52d | -483.7K | RH:1 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3.6-35b-a3b | [base: qwen3.6-35b-a3b] | 2026-04-15 | | | | | | |
| | RedHatAI/Qwen3.6-35B-A3B-FP8 | 2026-05-01 | 26.1K | | | | | |
| | RedHatAI/Qwen3.6-35B-A3B-FP8-dynamic | 2026-04-27 | 27.1K | | | | | |
| | RedHatAI/Qwen3.6-35B-A3B-NVFP4 | 2026-04-17 | 6.5M | | | | | |
| | nvidia/Qwen3.6-35B-A3B-NVFP4 | 2026-05-27 | 8.5M | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -40d | -2.0M | RH:3 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| gemma-4-26b-a4b | [base: gemma-4-26b-a4b] | 2026-03-11 | | | | | | |
| | RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic | 2026-04-06 | 2.7M | | | | | |
| | RedHatAI/gemma-4-26B-A4B-it-NVFP4 | 2026-04-05 | 2.6M | | | | | |
| | nvidia/Gemma-4-26B-A4B-NVFP4 | 2026-05-01 | 3.8M | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -26d | +1.5M | RH:2 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| gemma-4-31b | [base: gemma-4-31b] | 2026-03-11 | | | | | | |
| | RedHatAI/gemma-4-31B-it-FP8-block | 2026-04-03 | 4.4M | | | | | |
| | RedHatAI/gemma-4-31B-it-FP8-dynamic | 2026-04-06 | 792.7K | | | | | |
| | RedHatAI/gemma-4-31B-it-NVFP4 | 2026-04-03 | 742.7K | | | | | |
| | nvidia/Gemma-4-31B-IT-NVFP4 | 2026-04-02 | 6.6M | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +1d | -740.5K | RH:3 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| nvidia-nemotron-3-super-120b-a12b | [base: nvidia-nemotron-3-super-120b-a12b] | 2026-03-06 | | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 | 2026-03-26 | 5.1K | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Super-120B-A12B-FP8-Dflash | 2026-06-01 | 138 | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | 2026-03-27 | 11.3K | | | | | |
| | nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 | 2026-03-10 | 2.2M | | | | | |
| | nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | 2026-03-10 | 5.5M | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +16d | -7.8M | RH:3 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3.5-122b-a10b | [base: qwen3.5-122b-a10b] | 2026-02-24 | | | | | | |
| | RedHatAI/Qwen3.5-122B-A10B-FP8-dynamic | 2026-03-03 | 7.6K | | | | | |
| | RedHatAI/Qwen3.5-122B-A10B-NVFP4 | 2026-03-17 | 569.6K | | | | | |
| | nvidia/Qwen3.5-122B-A10B-NVFP4 | 2026-05-13 | 93.9K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -71d | +483.3K | RH:2 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3.5-397b-a17b | [base: qwen3.5-397b-a17b] | 2026-02-16 | | | | | | |
| | RedHatAI/Qwen3.5-397B-A17B-FP8-dynamic | 2026-02-18 | 11.8K | | | | | |
| | nvidia/Qwen3.5-397B-A17B-NVFP4 | 2026-02-16 | 2.4M | | | | | |
| | nvidia/Qwen3.5-397B-A17B-NVFP4-V2 | 2026-06-29 | 19.3K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +2d | -2.4M | RH:1 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-next-80b-a3b-thinking | [base: qwen3-next-80b-a3b-thinking] | 2025-09-09 | | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Thinking-FP8-block | 2025-12-24 | 138 | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Thinking-FP8-dynamic | 2025-12-24 | 1.1K | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Thinking-NVFP4 | 2025-12-24 | 1.7K | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Thinking-quantized.w4a16 | 2025-12-24 | 572 | | | | | |
| | nvidia/Qwen3-Next-80B-A3B-Thinking-NVFP4 | 2025-12-11 | 210.3K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +13d | -206.8K | RH:4 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-next-80b-a3b-instruct | [base: qwen3-next-80b-a3b-instruct] | 2025-09-09 | | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Instruct-FP8 | 2025-12-24 | 5.7K | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Instruct-FP8-block | 2025-12-12 | 87 | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Instruct-FP8-dynamic | 2025-12-12 | 214 | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Instruct-NVFP4 | 2025-12-12 | 79.1K | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 | 2025-12-12 | 2.9K | | | | | |
| | RedHatAI/Qwen3-Next-80B-A3B-Instruct-quantized.w8a8 | 2026-03-03 | 455 | | | | | |
| | nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4 | 2025-12-09 | 218.1K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +3d | -129.7K | RH:6 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| nvidia-nemotron-3-nano-30b-a3b | [base: nvidia-nemotron-3-nano-30b-a3b] | 2025-12-03 | | | | | | |
| | RedHatAI/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 | 2025-12-23 | 9.2K | | | | | |
| | nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 | 2025-12-06 | 5.9M | | | | | |
| | nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 | 2025-12-20 | 3.7M | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +17d | -9.6M | RH:1 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-vl-235b-a22b-instruct | [base: qwen3-vl-235b-a22b-instruct] | 2025-09-22 | | | | | | |
| | RedHatAI/Qwen3-VL-235B-A22B-Instruct-FP8-block | 2025-09-28 | 1.2K | | | | | |
| | RedHatAI/Qwen3-VL-235B-A22B-Instruct-FP8-dynamic | 2025-09-28 | 29.6K | | | | | |
| | RedHatAI/Qwen3-VL-235B-A22B-Instruct-NVFP4 | 2025-10-14 | 55.9K | | | | | |
| | nvidia/Qwen3-VL-235B-A22B-Instruct-NVFP4 | 2025-12-25 | 16.2K | | | | | |
| | nvidia/Qwen3-VL-235B-A22B-Instruct-NVFP4-MLPerf-Inference-Closed-V6.0 | 2026-01-27 | 190.0K | | | | | |
| | nvidia/Qwen3-VL-235B-A22B-Instruct-NVFP4-MLPerf-Inference-Closed-V6.1 | 2026-04-07 | 6.1K | | | | | |
| | nvidia/Qwen3-VL-235B-A22B-Instruct-NVFP4-MLPerf-Inference-Closed-V6.1-FP8-KV | 2026-06-15 | 18.9K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -88d | -144.4K | RH:3 NV:4 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| nvidia-nemotron-nano-9b-v2 | [base: nvidia-nemotron-nano-9b-v2] | 2025-08-12 | | | | | | |
| | RedHatAI/NVIDIA-Nemotron-Nano-9B-v2-FP8-dynamic | 2025-09-30 | 33.5K | | | | | |
| | RedHatAI/NVIDIA-Nemotron-Nano-9B-v2-quantized.w4a16 | 2025-10-22 | 8.3K | | | | | |
| | nvidia/NVIDIA-Nemotron-Nano-9B-v2-FP8 | 2025-09-22 | 873.5K | | | | | |
| | nvidia/NVIDIA-Nemotron-Nano-9B-v2-NVFP4 | 2025-10-07 | 100.7K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +8d | -932.3K | RH:2 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-235b-a22b | [base: qwen3-235b-a22b] | 2025-04-27 | | | | | | |
| | RedHatAI/Qwen3-235B-A22B-FP8-dynamic | 2025-05-04 | 31.0K | | | | | |
| | RedHatAI/Qwen3-235B-A22B-NVFP4 | 2025-10-29 | 1.3K | | | | | |
| | nvidia/Qwen3-235B-A22B-FP8 | 2025-07-08 | 11.5K | | | | | |
| | nvidia/Qwen3-235B-A22B-NVFP4 | 2025-07-08 | 86.9K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -65d | -66.0K | RH:2 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-30b-a3b | [base: qwen3-30b-a3b] | 2025-04-27 | | | | | | |
| | RedHatAI/Qwen3-30B-A3B-FP8-block | 2025-07-01 | 96.2K | | | | | |
| | RedHatAI/Qwen3-30B-A3B-FP8-dynamic | 2025-05-03 | 200.2K | | | | | |
| | RedHatAI/Qwen3-30B-A3B-NVFP4 | 2025-06-17 | 190.1K | | | | | |
| | RedHatAI/Qwen3-30B-A3B-quantized.w4a16 | 2025-05-06 | 81.6K | | | | | |
| | nvidia/Qwen3-30B-A3B-NVFP4 | 2025-07-08 | 705.0K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -66d | -136.9K | RH:4 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-8b | [base: qwen3-8b] | 2025-04-27 | | | | | | |
| | RedHatAI/Qwen3-8B-FP8-block | 2025-10-12 | 936 | | | | | |
| | RedHatAI/Qwen3-8B-FP8-dynamic | 2025-05-02 | 277.8K | | | | | |
| | RedHatAI/Qwen3-8B-NVFP4 | 2025-10-23 | 27.8K | | | | | |
| | RedHatAI/Qwen3-8B-quantized.w4a16 | 2025-05-06 | 161.0K | | | | | |
| | nvidia/Qwen3-8B-FP8 | 2025-09-09 | 93.1K | | | | | |
| | nvidia/Qwen3-8B-NVFP4 | 2025-09-09 | 444.6K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -130d | -70.1K | RH:4 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-32b | [base: qwen3-32b] | 2025-04-27 | | | | | | |
| | RedHatAI/Qwen3-32B-FP8-block | 2025-10-12 | 173 | | | | | |
| | RedHatAI/Qwen3-32B-FP8-dynamic | 2025-05-02 | 136.8K | | | | | |
| | RedHatAI/Qwen3-32B-NVFP4 | 2025-06-27 | 139.9K | | | | | |
| | RedHatAI/Qwen3-32B-NVFP4A16 | 2025-06-27 | 36.5K | | | | | |
| | RedHatAI/Qwen3-32B-quantized.w4a16 | 2025-05-05 | 51.4K | | | | | |
| | nvidia/Qwen3-32B-NVFP4 | 2025-09-09 | 627.2K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -130d | -262.5K | RH:5 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen3-14b | [base: qwen3-14b] | 2025-04-27 | | | | | | |
| | RedHatAI/Qwen3-14B-FP8-block | 2025-10-12 | 262 | | | | | |
| | RedHatAI/Qwen3-14B-FP8-dynamic | 2025-05-02 | 11.8K | | | | | |
| | RedHatAI/Qwen3-14B-NVFP4 | 2025-10-23 | 75.2K | | | | | |
| | RedHatAI/Qwen3-14B-quantized.w4a16 | 2025-05-05 | 11.1K | | | | | |
| | nvidia/Qwen3-14B-FP8 | 2025-09-09 | 48.1K | | | | | |
| | nvidia/Qwen3-14B-NVFP4 | 2025-09-09 | 549.5K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -130d | -499.3K | RH:4 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| llama-4-maverick-17b-128e-instruct | [base: llama-4-maverick-17b-128e-instruct] | 2025-04-01 | | | | | | |
| | RedHatAI/Llama-4-Maverick-17B-128E-Instruct-FP8 | 2025-05-12 | 58.8K | | | | | |
| | RedHatAI/Llama-4-Maverick-17B-128E-Instruct-FP8-block | 2025-10-27 | 139 | | | | | |
| | RedHatAI/Llama-4-Maverick-17B-128E-Instruct-NVFP4 | 2025-10-27 | 10.0K | | | | | |
| | RedHatAI/Llama-4-Maverick-17B-128E-Instruct-quantized.w4a16 | 2025-06-12 | 23.0K | | | | | |
| | nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8 | 2025-04-14 | 15.4K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | +28d | +76.6K | RH:4 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| llama-4-scout-17b-16e-instruct | [base: llama-4-scout-17b-16e-instruct] | 2025-04-02 | | | | | | |
| | RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-block | 2025-10-23 | 724 | | | | | |
| | RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic | 2025-04-10 | 213.1K | | | | | |
| | RedHatAI/Llama-4-Scout-17B-16E-Instruct-NVFP4 | 2025-07-08 | 20.5K | | | | | |
| | RedHatAI/Llama-4-Scout-17B-16E-Instruct-quantized.w4a16 | 2025-04-25 | 672.4K | | | | | |
| | nvidia/Llama-4-Scout-17B-16E-Instruct-FP8 | 2025-04-14 | 2.0M | | | | | |
| | nvidia/Llama-4-Scout-17B-16E-Instruct-NVFP4 | 2025-04-14 | 584.4K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -4d | -1.7M | RH:4 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| qwen2.5-vl-7b-instruct | [base: qwen2.5-vl-7b-instruct] | 2025-01-26 | | | | | | |
| | RedHatAI/Qwen2.5-VL-7B-Instruct-FP8-Dynamic | 2025-02-06 | 338.1K | | | | | |
| | RedHatAI/Qwen2.5-VL-7B-Instruct-quantized.w4a16 | 2025-02-07 | 44.5K | | | | | |
| | RedHatAI/Qwen2.5-VL-7B-Instruct-quantized.w8a8 | 2025-02-07 | 100.2K | | | | | |
| | nvidia/Qwen2.5-VL-7B-Instruct-FP8 | 2025-09-10 | 11.7K | | | | | |
| | nvidia/Qwen2.5-VL-7B-Instruct-NVFP4 | 2025-09-10 | 392.2K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -216d | +79.0K | RH:3 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| llama-3.3-70b-instruct | [base: llama-3.3-70b-instruct] | 2024-11-26 | | | | | | |
| | RedHatAI/Llama-3.3-70B-Instruct-FP8-block | 2025-10-20 | 25.8K | | | | | |
| | RedHatAI/Llama-3.3-70B-Instruct-FP8-dynamic | 2024-12-11 | 2.9M | | | | | |
| | RedHatAI/Llama-3.3-70B-Instruct-NVFP4 | 2025-08-05 | 26.5K | | | | | |
| | RedHatAI/Llama-3.3-70B-Instruct-quantized.w4a16 | 2025-01-02 | 97.9K | | | | | |
| | RedHatAI/Llama-3.3-70B-Instruct-quantized.w8a8 | 2025-01-20 | 207.2K | | | | | |
| | nvidia/Llama-3.3-70B-Instruct-FP8 | 2025-05-05 | 652.4K | | | | | |
| | nvidia/Llama-3.3-70B-Instruct-NVFP4 | 2025-01-16 | 588.7K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -36d | +2.0M | RH:5 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| llama-3.2-3b-instruct | [base: llama-3.2-3b-instruct] | 2024-09-18 | | | | | | |
| | RedHatAI/Llama-3.2-3B-Instruct-FP8 | 2024-09-26 | 339.1K | | | | | |
| | RedHatAI/Llama-3.2-3B-Instruct-FP8-dynamic | 2024-09-25 | 78.9K | | | | | |
| | RedHatAI/Llama-3.2-3B-Instruct-quantized.w8a8 | 2024-09-25 | 64.6K | | | | | |
| | nvidia/Meta-Llama-3.2-3B-Instruct-ONNX-INT4 | 2024-11-15 | 0 | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -51d | +482.5K | RH:3 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| llama-3.1-405b-instruct | [base: llama-3.1-405b-instruct] | 2024-07-16 | | | | | | |
| | RedHatAI/Meta-Llama-3.1-405B-Instruct-FP8 | 2024-07-23 | 44.5K | | | | | |
| | RedHatAI/Meta-Llama-3.1-405B-Instruct-FP8-dynamic | 2024-07-23 | 70.3K | | | | | |
| | RedHatAI/Meta-Llama-3.1-405B-Instruct-quantized.w4a16 | 2024-08-09 | 23.8K | | | | | |
| | RedHatAI/Meta-Llama-3.1-405B-Instruct-quantized.w8a16 | 2024-08-19 | 2.0K | | | | | |
| | RedHatAI/Meta-Llama-3.1-405B-Instruct-quantized.w8a8 | 2024-08-19 | 4.2K | | | | | |
| | nvidia/Llama-3.1-405B-Instruct-FP8 | 2024-08-29 | 106.1K | | | | | |
| | nvidia/Llama-3.1-405B-Instruct-NVFP4 | 2025-01-16 | 63.2K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -37d | -24.5K | RH:5 NV:2 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| llama-3.1-70b-instruct | [base: llama-3.1-70b-instruct] | 2024-07-16 | | | | | | |
| | RedHatAI/Llama-3.1-70B-Instruct-NVFP4 | 2025-06-27 | 5.0K | | | | | |
| | RedHatAI/Llama-3.1-70B-Instruct-NVFP4A16 | 2025-06-27 | 183 | | | | | |
| | RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8 | 2024-07-23 | 1.4M | | | | | |
| | RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8-dynamic | 2024-07-23 | 96.0K | | | | | |
| | RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w4a16 | 2024-07-31 | 1.8M | | | | | |
| | RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w8a16 | 2024-07-24 | 14.5K | | | | | |
| | RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w8a8 | 2024-07-29 | 225.4K | | | | | |
| | nvidia/Llama-3.1-70B-Instruct-FP8 | 2024-08-29 | 119.6K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -37d | +3.5M | RH:7 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| llama-3.1-8b-instruct | [base: llama-3.1-8b-instruct] | 2024-07-18 | | | | | | |
| | RedHatAI/Llama-3.1-8B-Instruct-FP8-block | 2025-10-10 | 724 | | | | | |
| | RedHatAI/Llama-3.1-8B-Instruct-NVFP4 | 2025-10-23 | 128.1K | | | | | |
| | RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8 | 2024-07-23 | 6.3M | | | | | |
| | RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic | 2024-07-23 | 729.0K | | | | | |
| | RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 | 2024-07-26 | 1.5M | | | | | |
| | RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a16 | 2024-07-24 | 94.4K | | | | | |
| | RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 | 2024-07-24 | 424.0K | | | | | |
| | nvidia/Llama-3.1-8B-Instruct-FP8 | 2024-08-29 | 2.3M | | | | | |
| | nvidia/Llama-3.1-8B-Instruct-NVFP4 | 2025-09-05 | 1.0M | | | | | |
| | nvidia/Meta-Llama-3.1-8B-Instruct-ONNX-INT4 | 2024-11-15 | 2.0K | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -37d | +5.8M | RH:7 NV:3 | | |
| --- | --- | --- | --- | --- | --- | --- | --- | |
| mistral-7b-instruct-v0.3 | [base: mistral-7b-instruct-v0.3] | 2024-05-22 | | | | | | |
| | RedHatAI/Mistral-7B-Instruct-v0.3-FP8 | 2024-06-26 | 38.3K | | | | | |
| | RedHatAI/Mistral-7B-Instruct-v0.3-GPTQ-4bit | 2024-05-23 | 1.4M | | | | | |
| | RedHatAI/Mistral-7B-Instruct-v0.3-quantized.w4a16 | 2024-07-11 | 7.6K | | | | | |
| | RedHatAI/Mistral-7B-Instruct-v0.3-quantized.w8a16 | 2024-07-10 | 32.2K | | | | | |
| | RedHatAI/Mistral-7B-Instruct-v0.3-quantized.w8a8 | 2024-07-18 | 3.7K | | | | | |
| | nvidia/Mistral-7B-Instruct-v0.3-ONNX-INT4 | 2024-11-15 | 0 | | | | | |
| | ^ DELTA (earliest date, total DLs) | | | -176d | +1.5M | RH:5 NV:1 | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
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| #!/usr/bin/env python3 | |
| """Compare quantized models released by both RedHatAI and Nvidia on Hugging Face Hub.""" | |
| import os | |
| import re | |
| import sys | |
| from datetime import datetime | |
| import pandas as pd | |
| DOWNLOADS_THRESHOLD = 100_000 | |
| CSV_PATH = "hf_quant_dataset.csv" # Created by hf_download_model.py | |
| def fmt_downloads(n): | |
| if n >= 1_000_000: | |
| return f"{n / 1_000_000:.1f}M" | |
| if n >= 1_000: | |
| return f"{n / 1_000:.1f}K" | |
| return str(n) | |
| def delta_days(date_a, date_b): | |
| """Return signed days between two date strings (a - b). Positive means a is later.""" | |
| try: | |
| da = datetime.strptime(date_a, "%Y-%m-%d") | |
| db = datetime.strptime(date_b, "%Y-%m-%d") | |
| d = (da - db).days | |
| return f"{d:+d}d" | |
| except (ValueError, TypeError): | |
| return "N/A" | |
| def delta_downloads(dl_a, dl_b): | |
| """Return formatted difference (a - b) with sign.""" | |
| d = dl_a - dl_b | |
| sign = "+" if d > 0 else "" | |
| return f"{sign}{fmt_downloads(d)}" if d >= 0 else f"-{fmt_downloads(abs(d))}" | |
| def print_table(title, headers, rows): | |
| print(f"\n{'=' * 60}") | |
| print(f" {title}") | |
| print(f"{'=' * 60}") | |
| col_widths = [len(h) for h in headers] | |
| for row in rows: | |
| for i, cell in enumerate(row): | |
| col_widths[i] = max(col_widths[i], len(str(cell))) | |
| header_line = " | ".join(h.ljust(col_widths[i]) for i, h in enumerate(headers)) | |
| sep_line = "-|-".join("-" * col_widths[i] for i in range(len(headers))) | |
| print(header_line) | |
| print(sep_line) | |
| for row in rows: | |
| print(" | ".join(str(cell).ljust(col_widths[i]) for i, cell in enumerate(row))) | |
| def load_from_csv(): | |
| """Load data from CSV created by hf_download_model.py.""" | |
| if not os.path.exists(CSV_PATH): | |
| print(f"ERROR: {CSV_PATH} not found.") | |
| print(f"Please run hf_download_model.py first to fetch and cache the data.") | |
| sys.exit(1) | |
| df = pd.read_csv(CSV_PATH) | |
| rh, nv = {}, {} | |
| orig_cache = {} | |
| for _, row in df.iterrows(): | |
| info = { | |
| "downloads": row["downloads"], | |
| "created": row["created"], | |
| } | |
| if row["hub"] == "RedHatAI": | |
| rh[row["name"]] = info | |
| elif row["hub"] == "nvidia": | |
| nv[row["name"]] = info | |
| # Build cache of original dates | |
| if row["base_date"] != "FAILED": | |
| orig_cache[row["norm_base"]] = row["base_date"] | |
| return rh, nv, orig_cache | |
| def main(): | |
| print(f"Loading data from {CSV_PATH}...") | |
| rh, nv, orig_cache = load_from_csv() | |
| print(f" RedHatAI quantized: {len(rh)}") | |
| print(f" Nvidia quantized: {len(nv)}") | |
| print(f" Download threshold: >{fmt_downloads(DOWNLOADS_THRESHOLD)} combined") | |
| # Build normalized base groupings (needed for both tables) | |
| rh_by_norm, nv_by_norm = {}, {} | |
| for name in rh: | |
| # Use norm_base from CSV by loading it again | |
| pass | |
| # Reload CSV to get norm_base info | |
| df = pd.read_csv(CSV_PATH) | |
| for _, row in df.iterrows(): | |
| if row["hub"] == "RedHatAI": | |
| rh_by_norm.setdefault(row["norm_base"], []).append(row["name"]) | |
| elif row["hub"] == "nvidia": | |
| nv_by_norm.setdefault(row["norm_base"], []).append(row["name"]) | |
| def get_orig_date(norm_base): | |
| return orig_cache.get(norm_base, "N/A") | |
| # --- Exact matches --- | |
| exact_names = sorted(set(rh) & set(nv)) | |
| exact_rows = [] | |
| for name in exact_names: | |
| r, n = rh[name], nv[name] | |
| combined = r["downloads"] + n["downloads"] | |
| if combined < DOWNLOADS_THRESHOLD: | |
| continue | |
| # Find norm_base from df | |
| norm = df[df["name"] == name].iloc[0]["norm_base"] | |
| exact_rows.append(( | |
| name, | |
| get_orig_date(norm), | |
| r["created"], fmt_downloads(r["downloads"]), | |
| n["created"], fmt_downloads(n["downloads"]), | |
| delta_days(r["created"], n["created"]), | |
| delta_downloads(r["downloads"], n["downloads"]), | |
| )) | |
| exact_rows.sort(key=lambda r: min(rh[r[0]]["created"], nv[r[0]]["created"]), reverse=True) | |
| print_table( | |
| f"EXACT MATCHES ({len(exact_rows)} models, >{fmt_downloads(DOWNLOADS_THRESHOLD)} combined) [delta = RedHatAI minus Nvidia]", | |
| ["Model Name", "Base Date", "RH Date", "RH DLs", "NV Date", "NV DLs", "Date Delta", "DL Delta"], | |
| exact_rows, | |
| ) | |
| # --- Fuzzy matches --- | |
| def base_earliest_date(base): | |
| all_dates = [] | |
| for n in rh_by_norm.get(base, []): | |
| d = rh[n]["created"] | |
| if d != "N/A": | |
| all_dates.append(d) | |
| for n in nv_by_norm.get(base, []): | |
| d = nv[n]["created"] | |
| if d != "N/A": | |
| all_dates.append(d) | |
| return min(all_dates) if all_dates else "9999" | |
| common_bases_all = set(rh_by_norm) & set(nv_by_norm) | |
| common_bases = sorted(common_bases_all, key=base_earliest_date, reverse=True) | |
| fuzzy_rows = [] | |
| fuzzy_count = 0 | |
| for base in common_bases: | |
| r_names = sorted(rh_by_norm[base]) | |
| n_names = sorted(nv_by_norm[base]) | |
| if all(n in exact_names for n in r_names) and all(n in exact_names for n in n_names): | |
| continue | |
| rh_total_dl = sum(rh[n]["downloads"] for n in r_names) | |
| nv_total_dl = sum(nv[n]["downloads"] for n in n_names) | |
| if rh_total_dl + nv_total_dl < DOWNLOADS_THRESHOLD: | |
| continue | |
| fuzzy_count += 1 | |
| orig_date = get_orig_date(base) | |
| fuzzy_rows.append((base, f"[base: {base}]", orig_date, "", "", "", "", "")) | |
| for rn in r_names: | |
| r = rh[rn] | |
| fuzzy_rows.append(("", f" RedHatAI/{rn}", r["created"], fmt_downloads(r["downloads"]), "", "", "", "")) | |
| for nn in n_names: | |
| n = nv[nn] | |
| fuzzy_rows.append(("", f" nvidia/{nn}", n["created"], fmt_downloads(n["downloads"]), "", "", "", "")) | |
| rh_dates = [rh[n]["created"] for n in r_names] | |
| nv_dates = [nv[n]["created"] for n in n_names] | |
| rh_earliest = min(d for d in rh_dates if d != "N/A") if any(d != "N/A" for d in rh_dates) else "N/A" | |
| nv_earliest = min(d for d in nv_dates if d != "N/A") if any(d != "N/A" for d in nv_dates) else "N/A" | |
| fuzzy_rows.append(( | |
| "", " ^ DELTA (earliest date, total DLs)", "", "", | |
| delta_days(rh_earliest, nv_earliest), | |
| delta_downloads(rh_total_dl, nv_total_dl), | |
| f"RH:{len(r_names)} NV:{len(n_names)}", | |
| "", | |
| )) | |
| fuzzy_rows.append(("---", "---", "---", "---", "---", "---", "---", "---")) | |
| print_table( | |
| f"FUZZY MATCHES — same base model, different quant ({fuzzy_count} base models, >{fmt_downloads(DOWNLOADS_THRESHOLD)} combined) [delta = RH minus NV]", | |
| ["Base Model", "Full Name", "Date", "Downloads", "Date Delta", "DL Delta", "Variants", ""], | |
| fuzzy_rows, | |
| ) | |
| if __name__ == "__main__": | |
| main() |
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| hub | name | created | downloads | downloads_30d | quant_format | model_size_b | norm_base | base_date | base_downloads | |
|---|---|---|---|---|---|---|---|---|---|---|
| RedHatAI | gemma-4-31B-it-FP8-block | 2026-04-03 | 4358837 | 3797660 | fp8 | 31.0 | gemma-4-31b | 2026-03-11 | 33431223.0 | |
| RedHatAI | Meta-Llama-3.1-70B-Instruct-FP8 | 2024-07-23 | 1444439 | 45972 | fp8 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| RedHatAI | Sparse-Llama-3.1-8B-gsm8k-2of4-FP8-dynamic | 2024-11-14 | 923 | 12 | fp8 | 8.0 | sparse-llama-3.1-8b-gsm8k-2of4 | 2024-11-05 | 576.0 | |
| RedHatAI | Llama-3.3-70B-Instruct-FP8-dynamic | 2024-12-11 | 2850696 | 51419 | fp8 | 70.0 | llama-3.3-70b-instruct | 2024-11-26 | 12995295.0 | |
| RedHatAI | Llama-3.3-70B-Instruct-quantized.w4a16 | 2025-01-02 | 97927 | 14249 | w4a16 | 70.0 | llama-3.3-70b-instruct | 2024-11-26 | 12995295.0 | |
| RedHatAI | OmniCoder-9B-FP8-Dynamic | 2026-03-20 | 1195 | 122 | fp8 | 9.0 | omnicoder-9b | 2026-03-12 | 53215.0 | |
| RedHatAI | gemma-4-31B-it-NVFP4 | 2026-04-03 | 742673 | 353056 | nvfp4 | 31.0 | gemma-4-31b | 2026-03-11 | 33431223.0 | |
| RedHatAI | gemma-4-26B-A4B-it-NVFP4 | 2026-04-05 | 2564317 | 1119333 | nvfp4 | 26.0 | gemma-4-26b-a4b | 2026-03-11 | 34957706.0 | |
| RedHatAI | gemma-4-31B-it-FP8-dynamic | 2026-04-06 | 792733 | 399942 | fp8 | 31.0 | gemma-4-31b | 2026-03-11 | 33431223.0 | |
| RedHatAI | Qwen3.6-35B-A3B-NVFP4 | 2026-04-17 | 6481659 | 1938768 | nvfp4 | 35.0 | qwen3.6-35b-a3b | 2026-04-15 | 15661179.0 | |
| RedHatAI | Qwen3.6-35B-A3B-FP8-dynamic | 2026-04-27 | 27055 | 22586 | fp8 | 35.0 | qwen3.6-35b-a3b | 2026-04-15 | 15661179.0 | |
| RedHatAI | gemma-4-12B-it-NVFP4 | 2026-06-08 | 288995 | 288995 | nvfp4 | 12.0 | gemma-4-12b | 2026-05-23 | 3395580.0 | |
| RedHatAI | diffusiongemma-26B-A4B-it-FP8-dynamic | 2026-06-09 | 284459 | 284459 | fp8 | 26.0 | diffusiongemma-26b-a4b | 2026-06-09 | 1783259.0 | |
| RedHatAI | Llama-2-7b-ultrachat200k-pruned_50-quantized-deepsparse | 2024-03-15 | 278 | 3 | deepsparse | 7.0 | llama-2-7b-ultrachat200k-pruned-50 | 2024-03-15 | 720.0 | |
| RedHatAI | Llama-2-7b-ultrachat200k-pruned_70-quantized-deepsparse | 2024-03-15 | 427 | 7 | deepsparse | 7.0 | llama-2-7b-ultrachat200k-pruned-70 | 2024-03-15 | 1064.0 | |
| RedHatAI | Llama-2-7b-evol-code-alpaca-pruned_50-quantized-deepsparse | 2024-03-15 | 343 | 2 | deepsparse | 7.0 | llama-2-7b-evol-code-alpaca-pruned-50 | 2024-03-15 | 389.0 | |
| RedHatAI | Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse | 2024-03-15 | 324 | 3 | deepsparse | 7.0 | llama-2-7b-evol-code-alpaca-pruned-70 | 2024-03-15 | 357.0 | |
| RedHatAI | Llama-2-7b-dolphin-open_platypus-pruned_50-quantized-deepsparse | 2024-03-15 | 278 | 2 | deepsparse | 7.0 | llama-2-7b-dolphin-open-platypus-pruned-50 | 2024-03-15 | 353.0 | |
| RedHatAI | Llama-2-7b-dolphin-open_platypus-pruned_70-quantized-deepsparse | 2024-03-15 | 285 | 4 | deepsparse | 7.0 | llama-2-7b-dolphin-open-platypus-pruned-70 | 2024-03-15 | 553.0 | |
| RedHatAI | Meta-Llama-3-8B-Instruct-FP8 | 2024-04-25 | 188904 | 2605 | fp8 | 8.0 | llama-3-8b-instruct | 2024-04-17 | 44161480.0 | |
| RedHatAI | Mixtral-8x7B-Instruct-v0.1-AutoFP8 | 2024-04-25 | 31198 | 8 | fp8 | 7.0 | mixtral-8x7b-instruct-v0.1 | 2023-12-10 | 22498540.0 | |
| RedHatAI | Llama-2-7b-cnn-daily-mail-pruned_50-quantized-deepsparse | 2024-05-15 | 214 | 1 | deepsparse | 7.0 | llama-2-7b-cnn-daily-mail-pruned-50 | 2024-05-15 | 172.0 | |
| RedHatAI | Llama-2-7b-cnn-daily-mail-pruned_70-quantized-deepsparse | 2024-05-17 | 182 | 2 | deepsparse | 7.0 | llama-2-7b-cnn-daily-mail-pruned-70 | 2024-05-15 | 183.0 | |
| RedHatAI | Meta-Llama-3-8B-Instruct-FP8-KV | 2024-05-20 | 287325 | 31223 | fp8 | 8.0 | llama-3-8b-instruct | 2024-04-17 | 44161480.0 | |
| RedHatAI | Mistral-7B-Instruct-v0.3-GPTQ-4bit | 2024-05-23 | 1388401 | 1290 | gptq | 7.0 | mistral-7b-instruct-v0.3 | 2024-05-22 | 37068277.0 | |
| RedHatAI | Meta-Llama-3-70B-Instruct-FP8 | 2024-05-24 | 456760 | 846 | fp8 | 70.0 | llama-3-70b-instruct | 2024-04-17 | 6153278.0 | |
| RedHatAI | Qwen2-72B-Instruct-FP8 | 2024-06-06 | 23210 | 1178 | fp8 | 72.0 | qwen2-72b-instruct | 2024-05-28 | 1328438.0 | |
| RedHatAI | Mixtral-8x22B-Instruct-v0.1-AutoFP8 | 2024-06-11 | 5053 | 3 | fp8 | 22.0 | mixtral-8x22b-instruct-v0.1 | 2024-04-16 | 6175042.0 | |
| RedHatAI | Qwen2-0.5B-Instruct-FP8 | 2024-06-14 | 31144 | 1321 | fp8 | 0.5 | qwen2-0.5b-instruct | 2024-06-03 | 7873956.0 | |
| RedHatAI | Qwen2-1.5B-Instruct-FP8 | 2024-06-14 | 286747 | 46510 | fp8 | 1.5 | qwen2-1.5b-instruct | 2024-06-03 | 27154329.0 | |
| RedHatAI | Qwen2-7B-Instruct-FP8 | 2024-06-14 | 188406 | 665 | fp8 | 7.0 | qwen2-7b-instruct | 2024-06-04 | 24954191.0 | |
| RedHatAI | Meta-Llama-3-70B-Instruct-FP8-KV | 2024-06-26 | 4529 | 4 | fp8 | 70.0 | llama-3-70b-instruct | 2024-04-17 | 6153278.0 | |
| RedHatAI | Mistral-7B-Instruct-v0.3-FP8 | 2024-06-26 | 38333 | 3810 | fp8 | 7.0 | mistral-7b-instruct-v0.3 | 2024-05-22 | 37068277.0 | |
| RedHatAI | Llama-2-7b-chat-hf-FP8 | 2024-06-26 | 11243 | 47 | fp8 | 7.0 | llama-2-7b-chat-hf | 2023-07-13 | 30380953.0 | |
| RedHatAI | Llama-2-7b-chat-quantized.w8a16 | 2024-07-02 | 2589 | 7 | w8a16 | 7.0 | llama-2-7b-chat | 2023-07-09 | 1341.0 | |
| RedHatAI | Meta-Llama-3-8B-Instruct-quantized.w8a16 | 2024-07-02 | 118577 | 298 | w8a16 | 8.0 | llama-3-8b-instruct | 2024-04-17 | 44161480.0 | |
| RedHatAI | Meta-Llama-3-70B-Instruct-quantized.w8a16 | 2024-07-02 | 14463 | 18 | w8a16 | 70.0 | llama-3-70b-instruct | 2024-04-17 | 6153278.0 | |
| RedHatAI | Qwen2-0.5B-Instruct-quantized.w8a16 | 2024-07-02 | 1005 | 8 | w8a16 | 0.5 | qwen2-0.5b-instruct | 2024-06-03 | 7873956.0 | |
| RedHatAI | Qwen2-1.5B-Instruct-quantized.w8a16 | 2024-07-02 | 730 | 7 | w8a16 | 1.5 | qwen2-1.5b-instruct | 2024-06-03 | 27154329.0 | |
| RedHatAI | Qwen2-7B-Instruct-quantized.w8a16 | 2024-07-02 | 776 | 11 | w8a16 | 7.0 | qwen2-7b-instruct | 2024-06-04 | 24954191.0 | |
| RedHatAI | Qwen2-72B-Instruct-quantized.w8a16 | 2024-07-02 | 3624 | 5 | w8a16 | 72.0 | qwen2-72b-instruct | 2024-05-28 | 1328438.0 | |
| RedHatAI | gemma-2-9b-it-FP8 | 2024-07-08 | 50509 | 1630 | fp8 | 9.0 | gemma-2-9b | 2024-06-24 | 9881731.0 | |
| RedHatAI | Qwen2-0.5B-Instruct-quantized.w4a16 | 2024-07-09 | 4185 | 160 | w4a16 | 0.5 | qwen2-0.5b-instruct | 2024-06-03 | 7873956.0 | |
| RedHatAI | Qwen2-1.5B-Instruct-quantized.w4a16 | 2024-07-09 | 2416 | 18 | w4a16 | 1.5 | qwen2-1.5b-instruct | 2024-06-03 | 27154329.0 | |
| RedHatAI | Mistral-7B-Instruct-v0.3-quantized.w8a16 | 2024-07-10 | 32206 | 35 | w8a16 | 7.0 | mistral-7b-instruct-v0.3 | 2024-05-22 | 37068277.0 | |
| RedHatAI | Llama-2-7b-chat-quantized.w8a8 | 2024-07-11 | 14378 | 17 | w8a8 | 7.0 | llama-2-7b-chat | 2023-07-09 | 1341.0 | |
| RedHatAI | Meta-Llama-3-8B-Instruct-quantized.w8a8 | 2024-07-11 | 83186 | 517 | w8a8 | 8.0 | llama-3-8b-instruct | 2024-04-17 | 44161480.0 | |
| RedHatAI | Qwen2-1.5B-Instruct-quantized.w8a8 | 2024-07-11 | 27468 | 664 | w8a8 | 1.5 | qwen2-1.5b-instruct | 2024-06-03 | 27154329.0 | |
| RedHatAI | Qwen2-72B-Instruct-quantized.w4a16 | 2024-07-11 | 4475 | 11 | w4a16 | 72.0 | qwen2-72b-instruct | 2024-05-28 | 1328438.0 | |
| RedHatAI | Qwen2-7B-Instruct-quantized.w4a16 | 2024-07-11 | 4617 | 76 | w4a16 | 7.0 | qwen2-7b-instruct | 2024-06-04 | 24954191.0 | |
| RedHatAI | Llama-2-7b-chat-quantized.w4a16 | 2024-07-11 | 1574 | 6 | w4a16 | 7.0 | llama-2-7b-chat | 2023-07-09 | 1341.0 | |
| RedHatAI | Meta-Llama-3-70B-Instruct-quantized.w4a16 | 2024-07-11 | 582577 | 22 | w4a16 | 70.0 | llama-3-70b-instruct | 2024-04-17 | 6153278.0 | |
| RedHatAI | Meta-Llama-3-8B-Instruct-quantized.w4a16 | 2024-07-11 | 6476 | 235 | w4a16 | 8.0 | llama-3-8b-instruct | 2024-04-17 | 44161480.0 | |
| RedHatAI | Mistral-7B-Instruct-v0.3-quantized.w4a16 | 2024-07-11 | 7567 | 429 | w4a16 | 7.0 | mistral-7b-instruct-v0.3 | 2024-05-22 | 37068277.0 | |
| RedHatAI | Qwen2-0.5B-Instruct-quantized.w8a8 | 2024-07-12 | 5318 | 10 | w8a8 | 0.5 | qwen2-0.5b-instruct | 2024-06-03 | 7873956.0 | |
| RedHatAI | Qwen2-7B-Instruct-quantized.w8a8 | 2024-07-12 | 5553 | 11 | w8a8 | 7.0 | qwen2-7b-instruct | 2024-06-04 | 24954191.0 | |
| RedHatAI | Meta-Llama-3-70B-Instruct-quantized.w8a8 | 2024-07-14 | 850 | 29 | w8a8 | 70.0 | llama-3-70b-instruct | 2024-04-17 | 6153278.0 | |
| RedHatAI | Qwen2-72B-Instruct-quantized.w8a8 | 2024-07-15 | 2508 | 8 | w8a8 | 72.0 | qwen2-72b-instruct | 2024-05-28 | 1328438.0 | |
| RedHatAI | Qwen2-57B-A14B-Instruct-FP8 | 2024-07-16 | 17100 | 647 | fp8 | 57.0 | qwen2-57b-a14b-instruct | 2024-06-04 | 291585.0 | |
| RedHatAI | Mistral-7B-Instruct-v0.3-quantized.w8a8 | 2024-07-18 | 3721 | 311 | w8a8 | 7.0 | mistral-7b-instruct-v0.3 | 2024-05-22 | 37068277.0 | |
| RedHatAI | Meta-Llama-3.1-8B-Instruct-FP8 | 2024-07-23 | 6268917 | 545513 | fp8 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| RedHatAI | Meta-Llama-3.1-8B-Instruct-FP8-dynamic | 2024-07-23 | 728962 | 67934 | fp8 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| RedHatAI | Meta-Llama-3.1-70B-Instruct-FP8-dynamic | 2024-07-23 | 95960 | 16377 | fp8 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| RedHatAI | Meta-Llama-3.1-405B-Instruct-FP8 | 2024-07-23 | 44518 | 295 | fp8 | 405.0 | llama-3.1-405b-instruct | 2024-07-16 | 4205284.0 | |
| RedHatAI | Meta-Llama-3.1-405B-Instruct-FP8-dynamic | 2024-07-23 | 70336 | 2247 | fp8 | 405.0 | llama-3.1-405b-instruct | 2024-07-16 | 4205284.0 | |
| RedHatAI | Meta-Llama-3.1-8B-Instruct-quantized.w8a16 | 2024-07-24 | 94353 | 1898 | w8a16 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| RedHatAI | Meta-Llama-3.1-8B-Instruct-quantized.w8a8 | 2024-07-24 | 424040 | 37780 | w8a8 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| RedHatAI | Meta-Llama-3.1-70B-Instruct-quantized.w8a16 | 2024-07-24 | 14525 | 36 | w8a16 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| RedHatAI | Meta-Llama-3.1-8B-Instruct-quantized.w4a16 | 2024-07-26 | 1513783 | 67362 | w4a16 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| RedHatAI | Meta-Llama-3.1-70B-Instruct-quantized.w8a8 | 2024-07-29 | 225432 | 8859 | w8a8 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| RedHatAI | Meta-Llama-3.1-8B-FP8 | 2024-07-31 | 1373126 | 239165 | fp8 | 8.0 | llama-3.1-8b | 2024-07-14 | 25602084.0 | |
| RedHatAI | Meta-Llama-3.1-70B-FP8 | 2024-07-31 | 13046 | 240 | fp8 | 70.0 | llama-3.1-70b | 2024-07-14 | 2220136.0 | |
| RedHatAI | Meta-Llama-3.1-8B-quantized.w8a16 | 2024-07-31 | 37052 | 5 | w8a16 | 8.0 | llama-3.1-8b | 2024-07-14 | 25602084.0 | |
| RedHatAI | Meta-Llama-3.1-8B-quantized.w8a8 | 2024-07-31 | 19214 | 1312 | w8a8 | 8.0 | llama-3.1-8b | 2024-07-14 | 25602084.0 | |
| RedHatAI | Meta-Llama-3.1-70B-Instruct-quantized.w4a16 | 2024-07-31 | 1830651 | 125014 | w4a16 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| RedHatAI | starcoder2-15b-FP8 | 2024-07-31 | 8909 | 3 | fp8 | 15.0 | starcoder2-15b | 2024-02-20 | 558380.0 | |
| RedHatAI | starcoder2-7b-FP8 | 2024-07-31 | 441 | 7 | fp8 | 7.0 | starcoder2-7b | 2024-02-20 | 819423.0 | |
| RedHatAI | starcoder2-3b-FP8 | 2024-07-31 | 600 | 4 | fp8 | 3.0 | starcoder2-3b | 2023-11-29 | 7197486.0 | |
| RedHatAI | Meta-Llama-3.1-70B-quantized.w8a16 | 2024-08-01 | 26 | 6 | w8a16 | 70.0 | llama-3.1-70b | 2024-07-14 | 2220136.0 | |
| RedHatAI | Meta-Llama-3.1-405B-FP8 | 2024-08-01 | 1201 | 69 | fp8 | 405.0 | llama-3.1-405b | 2024-07-16 | 20971859.0 | |
| RedHatAI | Meta-Llama-3.1-70B-quantized.w8a8 | 2024-08-01 | 408 | 5 | w8a8 | 70.0 | llama-3.1-70b | 2024-07-14 | 2220136.0 | |
| RedHatAI | starcoder2-15b-quantized.w8a16 | 2024-08-01 | 1642 | 8 | w8a16 | 15.0 | starcoder2-15b | 2024-02-20 | 558380.0 | |
| RedHatAI | starcoder2-3b-quantized.w8a16 | 2024-08-01 | 397 | 9 | w8a16 | 3.0 | starcoder2-3b | 2023-11-29 | 7197486.0 | |
| RedHatAI | starcoder2-7b-quantized.w8a16 | 2024-08-01 | 439 | 9 | w8a16 | 7.0 | starcoder2-7b | 2024-02-20 | 819423.0 | |
| RedHatAI | starcoder2-3b-quantized.w8a8 | 2024-08-01 | 672 | 61 | w8a8 | 3.0 | starcoder2-3b | 2023-11-29 | 7197486.0 | |
| RedHatAI | starcoder2-7b-quantized.w8a8 | 2024-08-01 | 530 | 7 | w8a8 | 7.0 | starcoder2-7b | 2024-02-20 | 819423.0 | |
| RedHatAI | starcoder2-15b-quantized.w8a8 | 2024-08-01 | 453 | 12 | w8a8 | 15.0 | starcoder2-15b | 2024-02-20 | 558380.0 | |
| RedHatAI | gemma-2-2b-it-FP8 | 2024-08-07 | 16979 | 1080 | fp8 | 2.0 | gemma-2-2b | 2024-07-16 | 10055650.0 | |
| RedHatAI | Meta-Llama-3.1-405B-Instruct-quantized.w4a16 | 2024-08-09 | 23785 | 106 | w4a16 | 405.0 | llama-3.1-405b-instruct | 2024-07-16 | 4205284.0 | |
| RedHatAI | gemma-2-9b-it-quantized.w8a16 | 2024-08-13 | 16742 | 83 | w8a16 | 9.0 | gemma-2-9b | 2024-06-24 | 9881731.0 | |
| RedHatAI | gemma-2-2b-it-quantized.w8a16 | 2024-08-13 | 984 | 24 | w8a16 | 2.0 | gemma-2-2b | 2024-07-16 | 10055650.0 | |
| RedHatAI | gemma-2-2b-quantized.w8a16 | 2024-08-13 | 679 | 7 | w8a16 | 2.0 | gemma-2-2b | 2024-07-16 | 10055650.0 | |
| RedHatAI | gemma-2-9b-it-quantized.w4a16 | 2024-08-15 | 10309 | 264 | w4a16 | 9.0 | gemma-2-9b | 2024-06-24 | 9881731.0 | |
| RedHatAI | gemma-2-2b-it-quantized.w4a16 | 2024-08-15 | 19049 | 172 | w4a16 | 2.0 | gemma-2-2b | 2024-07-16 | 10055650.0 | |
| RedHatAI | SmolLM-1.7B-Instruct-quantized.w8a16 | 2024-08-16 | 2914 | 4 | w8a16 | 1.7 | smollm-1.7b-instruct | 2024-07-15 | 344858.0 | |
| RedHatAI | gemma-2-2b-it-quantized.w8a8 | 2024-08-16 | 7227 | 49 | w8a8 | 2.0 | gemma-2-2b | 2024-07-16 | 10055650.0 | |
| RedHatAI | gemma-2-9b-it-quantized.w8a8 | 2024-08-16 | 2568 | 161 | w8a8 | 9.0 | gemma-2-9b | 2024-06-24 | 9881731.0 | |
| RedHatAI | Meta-Llama-3.1-405B-Instruct-quantized.w8a8 | 2024-08-19 | 4157 | 17 | w8a8 | 405.0 | llama-3.1-405b-instruct | 2024-07-16 | 4205284.0 | |
| RedHatAI | Meta-Llama-3.1-405B-Instruct-quantized.w8a16 | 2024-08-19 | 2036 | 12 | w8a16 | 405.0 | llama-3.1-405b-instruct | 2024-07-16 | 4205284.0 | |
| RedHatAI | gemma-2-27b-it-quantized.w8a16 | 2024-08-22 | 224 | 5 | w8a16 | 27.0 | gemma-2-27b | 2024-06-24 | 5154404.0 | |
| RedHatAI | SmolLM-1.7B-Instruct-quantized.w8a8 | 2024-08-23 | 432 | 2 | w8a8 | 1.7 | smollm-1.7b-instruct | 2024-07-15 | 344858.0 | |
| RedHatAI | Llama-3.2-1B-Instruct-FP8-dynamic | 2024-09-25 | 11682743 | 1639556 | fp8 | 1.0 | llama-3.2-1b-instruct | 2024-09-18 | 82420560.0 | |
| RedHatAI | Llama-3.2-3B-Instruct-FP8-dynamic | 2024-09-25 | 78893 | 824 | fp8 | 3.0 | llama-3.2-3b-instruct | 2024-09-18 | 45159369.0 | |
| RedHatAI | Llama-3.2-1B-Instruct-quantized.w8a8 | 2024-09-25 | 431906 | 43652 | w8a8 | 1.0 | llama-3.2-1b-instruct | 2024-09-18 | 82420560.0 | |
| RedHatAI | Llama-3.2-3B-Instruct-quantized.w8a8 | 2024-09-25 | 64556 | 3418 | w8a8 | 3.0 | llama-3.2-3b-instruct | 2024-09-18 | 45159369.0 | |
| RedHatAI | Llama-3.2-90B-Vision-Instruct-FP8-dynamic | 2024-09-25 | 444695 | 1861 | fp8 | 90.0 | llama-3.2-90b-vision-instruct | 2024-09-19 | 742732.0 | |
| RedHatAI | Llama-3.2-1B-Instruct-FP8 | 2024-09-26 | 5714955 | 841598 | fp8 | 1.0 | llama-3.2-1b-instruct | 2024-09-18 | 82420560.0 | |
| RedHatAI | Llama-3.2-3B-Instruct-FP8 | 2024-09-26 | 339082 | 1095 | fp8 | 3.0 | llama-3.2-3b-instruct | 2024-09-18 | 45159369.0 | |
| RedHatAI | Llama-3.2-1B-quantized.w8a8 | 2024-09-26 | 360469 | 64487 | w8a8 | 1.0 | llama-3.2-1b | 2024-09-18 | 55627999.0 | |
| RedHatAI | Llama-3.2-3B-quantized.w8a8 | 2024-09-26 | 38295 | 4013 | w8a8 | 3.0 | llama-3.2-3b | 2024-09-18 | 13619348.0 | |
| RedHatAI | Llama-3.2-1B-FP8 | 2024-09-26 | 240993 | 34836 | fp8 | 1.0 | llama-3.2-1b | 2024-09-18 | 55627999.0 | |
| RedHatAI | Qwen2.5-0.5B-quantized.w8a8 | 2024-10-09 | 2608 | 586 | w8a8 | 0.5 | qwen2.5-0.5b | 2024-09-15 | 24544068.0 | |
| RedHatAI | Qwen2.5-0.5B-Instruct-quantized.w8a8 | 2024-10-09 | 8479 | 70 | w8a8 | 0.5 | qwen2.5-0.5b-instruct | 2024-09-16 | 50430710.0 | |
| RedHatAI | Qwen2.5-1.5B-quantized.w8a8 | 2024-10-09 | 6618774 | 929780 | w8a8 | 1.5 | qwen2.5-1.5b | 2024-09-15 | 12501141.0 | |
| RedHatAI | Qwen2.5-1.5B-Instruct-quantized.w8a8 | 2024-10-09 | 692 | 31 | w8a8 | 1.5 | qwen2.5-1.5b-instruct | 2024-09-17 | 192429890.0 | |
| RedHatAI | Qwen2.5-3B-Instruct-quantized.w8a8 | 2024-10-09 | 984 | 29 | w8a8 | 3.0 | qwen2.5-3b-instruct | 2024-09-17 | 104024465.0 | |
| RedHatAI | Qwen2.5-7B-Instruct-quantized.w8a8 | 2024-10-09 | 16071 | 774 | w8a8 | 7.0 | qwen2.5-7b-instruct | 2024-09-16 | 153771461.0 | |
| RedHatAI | Qwen2.5-7B-quantized.w8a8 | 2024-10-09 | 7965 | 29 | w8a8 | 7.0 | qwen2.5-7b | 2024-09-15 | 19197266.0 | |
| RedHatAI | Qwen2.5-32B-quantized.w8a8 | 2024-10-09 | 22 | 6 | w8a8 | 32.0 | qwen2.5-32b | 2024-09-15 | 4145585.0 | |
| RedHatAI | Qwen2.5-32B-Instruct-quantized.w8a8 | 2024-10-09 | 2571 | 187 | w8a8 | 32.0 | qwen2.5-32b-instruct | 2024-09-17 | 25868299.0 | |
| RedHatAI | Qwen2.5-72B-Instruct-quantized.w8a8 | 2024-10-09 | 1918 | 136 | w8a8 | 72.0 | qwen2.5-72b-instruct | 2024-09-16 | 7726199.0 | |
| RedHatAI | Qwen2.5-72B-quantized.w8a8 | 2024-10-09 | 374 | 4 | w8a8 | 72.0 | qwen2.5-72b | 2024-09-15 | 660757.0 | |
| RedHatAI | Qwen2.5-0.5B-quantized.w8a16 | 2024-10-09 | 2271 | 1548 | w8a16 | 0.5 | qwen2.5-0.5b | 2024-09-15 | 24544068.0 | |
| RedHatAI | Qwen2.5-0.5B-Instruct-quantized.w8a16 | 2024-10-09 | 86 | 13 | w8a16 | 0.5 | qwen2.5-0.5b-instruct | 2024-09-16 | 50430710.0 | |
| RedHatAI | Qwen2.5-1.5B-quantized.w8a16 | 2024-10-09 | 453 | 77 | w8a16 | 1.5 | qwen2.5-1.5b | 2024-09-15 | 12501141.0 | |
| RedHatAI | Qwen2.5-3B-quantized.w8a16 | 2024-10-09 | 397 | 229 | w8a16 | 3.0 | qwen2.5-3b | 2024-09-15 | 6436856.0 | |
| RedHatAI | Qwen2.5-7B-quantized.w8a16 | 2024-10-09 | 558 | 272 | w8a16 | 7.0 | qwen2.5-7b | 2024-09-15 | 19197266.0 | |
| RedHatAI | Qwen2.5-7B-Instruct-quantized.w8a16 | 2024-10-09 | 25486 | 5768 | w8a16 | 7.0 | qwen2.5-7b-instruct | 2024-09-16 | 153771461.0 | |
| RedHatAI | Qwen2.5-32B-Instruct-quantized.w8a16 | 2024-10-09 | 44 | 4 | w8a16 | 32.0 | qwen2.5-32b-instruct | 2024-09-17 | 25868299.0 | |
| RedHatAI | Qwen2.5-32B-quantized.w8a16 | 2024-10-09 | 605 | 120 | w8a16 | 32.0 | qwen2.5-32b | 2024-09-15 | 4145585.0 | |
| RedHatAI | Qwen2.5-72B-quantized.w8a16 | 2024-10-09 | 229 | 5 | w8a16 | 72.0 | qwen2.5-72b | 2024-09-15 | 660757.0 | |
| RedHatAI | pixtral-12b-FP8-dynamic | 2024-10-10 | 265115 | 988 | fp8 | 12.0 | pixtral-12b | 2024-09-11 | 105126.0 | |
| RedHatAI | Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic | 2024-10-16 | 172238 | 267 | fp8 | 70.0 | llama-3.1-nemotron-70b-instruct-hf | 2024-10-12 | 1871913.0 | |
| RedHatAI | Qwen2.5-3B-Instruct-quantized.w8a16 | 2024-10-17 | 117 | 4 | w8a16 | 3.0 | qwen2.5-3b-instruct | 2024-09-17 | 104024465.0 | |
| RedHatAI | Qwen2.5-1.5B-Instruct-quantized.w8a16 | 2024-10-17 | 328 | 8 | w8a16 | 1.5 | qwen2.5-1.5b-instruct | 2024-09-17 | 192429890.0 | |
| RedHatAI | Qwen2.5-72B-Instruct-quantized.w8a16 | 2024-10-17 | 102 | 4 | w8a16 | 72.0 | qwen2.5-72b-instruct | 2024-09-16 | 7726199.0 | |
| RedHatAI | Qwen2.5-72B-Instruct-quantized.w4a16 | 2024-10-17 | 1073 | 25 | w4a16 | 72.0 | qwen2.5-72b-instruct | 2024-09-16 | 7726199.0 | |
| RedHatAI | Qwen2.5-72B-quantized.w4a16 | 2024-10-17 | 18 | 3 | w4a16 | 72.0 | qwen2.5-72b | 2024-09-15 | 660757.0 | |
| RedHatAI | Qwen2.5-32B-quantized.w4a16 | 2024-10-18 | 21 | 1 | w4a16 | 32.0 | qwen2.5-32b | 2024-09-15 | 4145585.0 | |
| RedHatAI | Qwen2.5-32B-Instruct-quantized.w4a16 | 2024-10-18 | 595 | 103 | w4a16 | 32.0 | qwen2.5-32b-instruct | 2024-09-17 | 25868299.0 | |
| RedHatAI | Qwen2.5-7B-quantized.w4a16 | 2024-10-18 | 1242 | 7 | w4a16 | 7.0 | qwen2.5-7b | 2024-09-15 | 19197266.0 | |
| RedHatAI | granite-3.0-8b-instruct-GGUF | 2024-10-31 | 108 | 3 | gguf | 8.0 | granite-3.0-8b-instruct | 2024-10-02 | 773522.0 | |
| RedHatAI | granite-3.0-3b-a800m-instruct-GGUF | 2024-11-01 | 103 | 3 | gguf | 3.0 | granite-3.0-3b-a800m-instruct | 2024-10-03 | 72689.0 | |
| RedHatAI | granite-3.0-2b-instruct-GGUF | 2024-11-01 | 135 | 2 | gguf | 2.0 | granite-3.0-2b-instruct | 2024-10-02 | 209079.0 | |
| RedHatAI | granite-3.0-1b-a400m-instruct-GGUF | 2024-11-01 | 138 | 5 | gguf | 1.0 | granite-3.0-1b-a400m-instruct | 2024-10-03 | 25435.0 | |
| RedHatAI | Qwen2-VL-72B-Instruct-FP8-dynamic | 2024-11-14 | 3710 | 45 | fp8 | 72.0 | qwen2-vl-72b-instruct | 2024-09-17 | 1031300.0 | |
| RedHatAI | Sparse-Llama-3.1-8B-evolcodealpaca-2of4-FP8-dynamic | 2024-11-15 | 230 | 2 | fp8 | 8.0 | sparse-llama-3.1-8b-evolcodealpaca-2of4 | 2024-11-21 | 358.0 | |
| RedHatAI | Sparse-Llama-3.1-8B-ultrachat_200k-2of4-FP8-dynamic | 2024-11-19 | 318 | 5 | fp8 | 8.0 | sparse-llama-3.1-8b-ultrachat-200k-2of4 | 2024-11-21 | 446.0 | |
| RedHatAI | Sparse-Llama-3.1-8B-ultrachat_200k-2of4-quantized.w4a16 | 2024-11-20 | 2838 | 7 | w4a16 | 8.0 | sparse-llama-3.1-8b-ultrachat-200k-2of4 | 2024-11-21 | 446.0 | |
| RedHatAI | Sparse-Llama-3.1-8B-evolcodealpaca-2of4-quantized.w4a16 | 2024-11-21 | 280 | 4 | w4a16 | 8.0 | sparse-llama-3.1-8b-evolcodealpaca-2of4 | 2024-11-21 | 358.0 | |
| RedHatAI | Sparse-Llama-3.1-8B-gsm8k-2of4-quantized.w4a16 | 2024-11-21 | 319 | 4 | w4a16 | 8.0 | sparse-llama-3.1-8b-gsm8k-2of4 | 2024-11-05 | 576.0 | |
| RedHatAI | Qwen2.5-0.5B-FP8-dynamic | 2024-11-27 | 2615 | 232 | fp8 | 0.5 | qwen2.5-0.5b | 2024-09-15 | 24544068.0 | |
| RedHatAI | Qwen2.5-0.5B-Instruct-FP8-dynamic | 2024-11-27 | 316 | 110 | fp8 | 0.5 | qwen2.5-0.5b-instruct | 2024-09-16 | 50430710.0 | |
| RedHatAI | Qwen2.5-1.5B-FP8-dynamic | 2024-11-27 | 4965 | 26 | fp8 | 1.5 | qwen2.5-1.5b | 2024-09-15 | 12501141.0 | |
| RedHatAI | Qwen2.5-1.5B-Instruct-FP8-dynamic | 2024-11-27 | 167 | 22 | fp8 | 1.5 | qwen2.5-1.5b-instruct | 2024-09-17 | 192429890.0 | |
| RedHatAI | Qwen2.5-3B-FP8-dynamic | 2024-11-27 | 641 | 98 | fp8 | 3.0 | qwen2.5-3b | 2024-09-15 | 6436856.0 | |
| RedHatAI | Qwen2.5-3B-Instruct-FP8-dynamic | 2024-11-27 | 1742 | 103 | fp8 | 3.0 | qwen2.5-3b-instruct | 2024-09-17 | 104024465.0 | |
| RedHatAI | Qwen2.5-7B-FP8-dynamic | 2024-11-27 | 3510 | 41 | fp8 | 7.0 | qwen2.5-7b | 2024-09-15 | 19197266.0 | |
| RedHatAI | Qwen2.5-7B-Instruct-FP8-dynamic | 2024-11-27 | 57752 | 8060 | fp8 | 7.0 | qwen2.5-7b-instruct | 2024-09-16 | 153771461.0 | |
| RedHatAI | Qwen2.5-32B-Instruct-FP8-dynamic | 2024-11-27 | 8089 | 1178 | fp8 | 32.0 | qwen2.5-32b-instruct | 2024-09-17 | 25868299.0 | |
| RedHatAI | Qwen2.5-32B-FP8-dynamic | 2024-11-28 | 18 | 2 | fp8 | 32.0 | qwen2.5-32b | 2024-09-15 | 4145585.0 | |
| RedHatAI | Qwen2.5-72B-FP8-dynamic | 2024-11-28 | 5734 | 40 | fp8 | 72.0 | qwen2.5-72b | 2024-09-15 | 660757.0 | |
| RedHatAI | Qwen2.5-Math-72B-FP8-dynamic | 2024-11-28 | 15 | 2 | fp8 | 72.0 | qwen2.5-math-72b | 2024-09-16 | 50378.0 | |
| RedHatAI | Qwen2.5-Math-72B-Instruct-FP8-dynamic | 2024-11-28 | 170 | 89 | fp8 | 72.0 | qwen2.5-math-72b-instruct | 2024-09-16 | 134870.0 | |
| RedHatAI | Qwen2.5-Coder-32B-FP8-dynamic | 2024-11-28 | 276 | 3 | fp8 | 32.0 | qwen2.5-coder-32b | 2024-11-08 | 363150.0 | |
| RedHatAI | Qwen2.5-Coder-32B-Instruct-FP8-dynamic | 2024-11-28 | 222444 | 14543 | fp8 | 32.0 | qwen2.5-coder-32b-instruct | 2024-11-06 | 9512265.0 | |
| RedHatAI | Qwen2.5-Coder-14B-Instruct-FP8-dynamic | 2024-11-28 | 139973 | 1901 | fp8 | 14.0 | qwen2.5-coder-14b-instruct | 2024-11-06 | 10953355.0 | |
| RedHatAI | Qwen2.5-Coder-14B-FP8-dynamic | 2024-11-28 | 73 | 3 | fp8 | 14.0 | qwen2.5-coder-14b | 2024-11-08 | 309802.0 | |
| RedHatAI | Qwen2.5-Math-7B-FP8-dynamic | 2024-11-28 | 16 | 2 | fp8 | 7.0 | qwen2.5-math-7b | 2024-09-16 | 3542980.0 | |
| RedHatAI | Qwen2.5-Coder-7B-Instruct-FP8-dynamic | 2024-11-28 | 5866 | 514 | fp8 | 7.0 | qwen2.5-coder-7b-instruct | 2024-09-17 | 17413961.0 | |
| RedHatAI | Qwen2.5-Coder-7B-FP8-dynamic | 2024-11-28 | 376 | 102 | fp8 | 7.0 | qwen2.5-coder-7b | 2024-09-16 | 3970148.0 | |
| RedHatAI | Qwen2.5-72B-Instruct-FP8-dynamic | 2024-12-02 | 58713 | 37675 | fp8 | 72.0 | qwen2.5-72b-instruct | 2024-09-16 | 7726199.0 | |
| RedHatAI | Qwen2.5-14B-Instruct-FP8-dynamic | 2024-12-02 | 241833 | 32794 | fp8 | 14.0 | qwen2.5-14b-instruct | 2024-09-16 | 41315030.0 | |
| RedHatAI | Qwen2.5-14B-FP8-dynamic | 2024-12-02 | 2808 | 129 | fp8 | 14.0 | qwen2.5-14b | 2024-09-15 | 3784685.0 | |
| RedHatAI | Qwen2.5-14B-quantized.w8a8 | 2024-12-03 | 529 | 3 | w8a8 | 14.0 | qwen2.5-14b | 2024-09-15 | 3784685.0 | |
| RedHatAI | Qwen2.5-3B-quantized.w8a8 | 2024-12-03 | 236 | 4 | w8a8 | 3.0 | qwen2.5-3b | 2024-09-15 | 6436856.0 | |
| RedHatAI | Qwen2.5-14B-Instruct-quantized.w8a8 | 2024-12-10 | 6125 | 185 | w8a8 | 14.0 | qwen2.5-14b-instruct | 2024-09-16 | 41315030.0 | |
| RedHatAI | Qwen2.5-Math-7B-Instruct-FP8-dynamic | 2024-12-11 | 83 | 5 | fp8 | 7.0 | qwen2.5-math-7b-instruct | 2024-09-19 | 1780214.0 | |
| RedHatAI | Qwen2.5-0.5B-quantized.w4a16 | 2024-12-17 | 2158 | 84 | w4a16 | 0.5 | qwen2.5-0.5b | 2024-09-15 | 24544068.0 | |
| RedHatAI | granite-3.1-8b-instruct-GGUF | 2024-12-17 | 209 | 6 | gguf | 8.0 | granite-3.1-8b-instruct | 2024-12-06 | 1518844.0 | |
| RedHatAI | Qwen2.5-1.5B-quantized.w4a16 | 2024-12-17 | 844 | 17 | w4a16 | 1.5 | qwen2.5-1.5b | 2024-09-15 | 12501141.0 | |
| RedHatAI | Qwen2.5-3B-quantized.w4a16 | 2024-12-17 | 1065 | 34 | w4a16 | 3.0 | qwen2.5-3b | 2024-09-15 | 6436856.0 | |
| RedHatAI | Mixtral-8x22B-v0.1-quantized.w4a16 | 2025-01-03 | 255 | 1 | w4a16 | 22.0 | mixtral-8x22b-v0.1 | 2024-04-16 | 4854835.0 | |
| RedHatAI | Mixtral-8x7B-v0.1-quantized.w4a16 | 2025-01-03 | 829 | 119 | w4a16 | 7.0 | mixtral-8x7b-v0.1 | 2023-12-01 | 8343512.0 | |
| RedHatAI | QwQ-32B-Preview-FP8-dynamic | 2025-01-03 | 26 | 3 | fp8 | 32.0 | qwq-32b-preview | 2024-11-27 | 1432887.0 | |
| RedHatAI | QwQ-32B-Preview-quantized.w4a16 | 2025-01-03 | 271 | 5 | w4a16 | 32.0 | qwq-32b-preview | 2024-11-27 | 1432887.0 | |
| RedHatAI | Llama-3.1-Nemotron-70B-Instruct-HF-quantized.w4a16 | 2025-01-03 | 192 | 13 | w4a16 | 70.0 | llama-3.1-nemotron-70b-instruct-hf | 2024-10-12 | 1871913.0 | |
| RedHatAI | Llama-3.1-Nemotron-70B-Instruct-HF-quantized.w8a8 | 2025-01-03 | 144 | 3 | w8a8 | 70.0 | llama-3.1-nemotron-70b-instruct-hf | 2024-10-12 | 1871913.0 | |
| RedHatAI | QwQ-32B-Preview-quantized.w8a8 | 2025-01-03 | 124 | 7 | w8a8 | 32.0 | qwq-32b-preview | 2024-11-27 | 1432887.0 | |
| RedHatAI | granite-3.1-8b-instruct-FP8-dynamic | 2025-01-07 | 3292 | 355 | fp8 | 8.0 | granite-3.1-8b-instruct | 2024-12-06 | 1518844.0 | |
| RedHatAI | granite-3.1-2b-instruct-FP8-dynamic | 2025-01-07 | 566 | 47 | fp8 | 2.0 | granite-3.1-2b-instruct | 2024-12-06 | 299163.0 | |
| RedHatAI | granite-3.1-8b-instruct-quantized.w4a16 | 2025-01-07 | 16110 | 1133 | w4a16 | 8.0 | granite-3.1-8b-instruct | 2024-12-06 | 1518844.0 | |
| RedHatAI | granite-3.1-8b-instruct-quantized.w8a8 | 2025-01-07 | 13889 | 206 | w8a8 | 8.0 | granite-3.1-8b-instruct | 2024-12-06 | 1518844.0 | |
| RedHatAI | granite-3.1-2b-instruct-quantized.w8a8 | 2025-01-07 | 6663 | 12 | w8a8 | 2.0 | granite-3.1-2b-instruct | 2024-12-06 | 299163.0 | |
| RedHatAI | granite-3.1-2b-instruct-quantized.w4a16 | 2025-01-07 | 192746 | 52587 | w4a16 | 2.0 | granite-3.1-2b-instruct | 2024-12-06 | 299163.0 | |
| RedHatAI | granite-3.1-2b-base-quantized.w8a8 | 2025-01-16 | 6518 | 9 | w8a8 | 2.0 | granite-3.1-2b-base | 2024-12-06 | 105380.0 | |
| RedHatAI | granite-3.1-2b-base-quantized.w4a16 | 2025-01-16 | 449 | 6 | w4a16 | 2.0 | granite-3.1-2b-base | 2024-12-06 | 105380.0 | |
| RedHatAI | granite-3.1-2b-base-FP8-dynamic | 2025-01-16 | 273 | 12 | fp8 | 2.0 | granite-3.1-2b-base | 2024-12-06 | 105380.0 | |
| RedHatAI | granite-3.1-8b-base-quantized.w8a8 | 2025-01-16 | 5535 | 9 | w8a8 | 8.0 | granite-3.1-8b-base | 2024-12-06 | 122056.0 | |
| RedHatAI | granite-3.1-8b-base-quantized.w4a16 | 2025-01-16 | 818 | 36 | w4a16 | 8.0 | granite-3.1-8b-base | 2024-12-06 | 122056.0 | |
| RedHatAI | granite-3.1-8b-base-FP8-dynamic | 2025-01-16 | 177 | 6 | fp8 | 8.0 | granite-3.1-8b-base | 2024-12-06 | 122056.0 | |
| RedHatAI | Llama-3.3-70B-Instruct-quantized.w8a8 | 2025-01-20 | 207192 | 9203 | w8a8 | 70.0 | llama-3.3-70b-instruct | 2024-11-26 | 12995295.0 | |
| RedHatAI | Qwen2-VL-72B-Instruct-quantized.w4a16 | 2025-01-23 | 878 | 13 | w4a16 | 72.0 | qwen2-vl-72b-instruct | 2024-09-17 | 1031300.0 | |
| RedHatAI | Mistral-Small-24B-Instruct-2501-FP8-dynamic | 2025-01-30 | 922403 | 2828 | fp8 | 24.0 | mistral-small-24b-instruct-2501 | 2025-01-28 | 7148090.0 | |
| RedHatAI | Mistral-Small-24B-Instruct-2501-quantized.w4a16 | 2025-01-31 | 13161 | 315 | w4a16 | 24.0 | mistral-small-24b-instruct-2501 | 2025-01-28 | 7148090.0 | |
| RedHatAI | DeepSeek-R1-Distill-Llama-8B-quantized.w8a8 | 2025-02-01 | 263788 | 317 | w8a8 | 8.0 | deepseek-r1-distill-llama-8b | 2025-01-20 | 18791366.0 | |
| RedHatAI | DeepSeek-R1-Distill-Llama-8B-quantized.w4a16 | 2025-02-01 | 22393 | 368 | w4a16 | 8.0 | deepseek-r1-distill-llama-8b | 2025-01-20 | 18791366.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-1.5B-FP8-dynamic | 2025-02-01 | 8617 | 449 | fp8 | 1.5 | deepseek-r1-distill-qwen-1.5b | 2025-01-20 | 19747914.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-7B-FP8-dynamic | 2025-02-01 | 30391 | 1251 | fp8 | 7.0 | deepseek-r1-distill-qwen-7b | 2025-01-20 | 14472661.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-14B-FP8-dynamic | 2025-02-01 | 36486 | 473 | fp8 | 14.0 | deepseek-r1-distill-qwen-14b | 2025-01-20 | 7940414.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic | 2025-02-01 | 57694 | 3675 | fp8 | 32.0 | deepseek-r1-distill-qwen-32b | 2025-01-20 | 25901239.0 | |
| RedHatAI | DeepSeek-R1-Distill-Llama-8B-FP8-dynamic | 2025-02-01 | 13086 | 392 | fp8 | 8.0 | deepseek-r1-distill-llama-8b | 2025-01-20 | 18791366.0 | |
| RedHatAI | DeepSeek-R1-Distill-Llama-70B-FP8-dynamic | 2025-02-01 | 490648 | 4846 | fp8 | 70.0 | deepseek-r1-distill-llama-70b | 2025-01-20 | 5285395.0 | |
| RedHatAI | Qwen2-VL-72B-Instruct-quantized.w8a8 | 2025-02-03 | 578 | 23 | w8a8 | 72.0 | qwen2-vl-72b-instruct | 2024-09-17 | 1031300.0 | |
| RedHatAI | DeepSeek-R1-Distill-Llama-70B-quantized.w8a8 | 2025-02-04 | 22856 | 223 | w8a8 | 70.0 | deepseek-r1-distill-llama-70b | 2025-01-20 | 5285395.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16 | 2025-02-04 | 13417 | 165 | w4a16 | 7.0 | deepseek-r1-distill-qwen-7b | 2025-01-20 | 14472661.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-14B-quantized.w8a8 | 2025-02-04 | 33128 | 20 | w8a8 | 14.0 | deepseek-r1-distill-qwen-14b | 2025-01-20 | 7940414.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-14B-quantized.w4a16 | 2025-02-04 | 16998 | 286 | w4a16 | 14.0 | deepseek-r1-distill-qwen-14b | 2025-01-20 | 7940414.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-32B-quantized.w4a16 | 2025-02-05 | 47667 | 682 | w4a16 | 32.0 | deepseek-r1-distill-qwen-32b | 2025-01-20 | 25901239.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-32B-quantized.w8a8 | 2025-02-05 | 33691 | 174 | w8a8 | 32.0 | deepseek-r1-distill-qwen-32b | 2025-01-20 | 25901239.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-7B-quantized.w8a8 | 2025-02-05 | 51240 | 293 | w8a8 | 7.0 | deepseek-r1-distill-qwen-7b | 2025-01-20 | 14472661.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-1.5B-quantized.w8a8 | 2025-02-06 | 50173 | 63 | w8a8 | 1.5 | deepseek-r1-distill-qwen-1.5b | 2025-01-20 | 19747914.0 | |
| RedHatAI | Qwen2.5-VL-3B-Instruct-FP8-dynamic | 2025-02-06 | 241416 | 69851 | fp8 | 3.0 | qwen2.5-vl-3b-instruct | 2025-01-26 | 88876421.0 | |
| RedHatAI | Qwen2.5-VL-7B-Instruct-FP8-Dynamic | 2025-02-06 | 338133 | 5390 | fp8 | 7.0 | qwen2.5-vl-7b-instruct | 2025-01-26 | 79189674.0 | |
| RedHatAI | Qwen2.5-VL-72B-Instruct-FP8-dynamic | 2025-02-06 | 359823 | 5024 | fp8 | 72.0 | qwen2.5-vl-72b-instruct | 2025-01-27 | 6744401.0 | |
| RedHatAI | DeepSeek-R1-Distill-Llama-70B-quantized.w4a16 | 2025-02-07 | 49344 | 6656 | w4a16 | 70.0 | deepseek-r1-distill-llama-70b | 2025-01-20 | 5285395.0 | |
| RedHatAI | DeepSeek-R1-Distill-Qwen-1.5B-quantized.w4a16 | 2025-02-07 | 6856 | 80 | w4a16 | 1.5 | deepseek-r1-distill-qwen-1.5b | 2025-01-20 | 19747914.0 | |
| RedHatAI | DeepSeek-R1-Llama-70B-quantized.w8a8 | 2025-02-07 | 220 | 3 | w8a8 | 70.0 | deepseek-r1-llama-70b | 2025-01-20 | 5285395.0 | |
| RedHatAI | Qwen2.5-VL-7B-Instruct-quantized.w4a16 | 2025-02-07 | 44505 | 3717 | w4a16 | 7.0 | qwen2.5-vl-7b-instruct | 2025-01-26 | 79189674.0 | |
| RedHatAI | Qwen2.5-VL-3B-Instruct-quantized.w4a16 | 2025-02-07 | 129671 | 115971 | w4a16 | 3.0 | qwen2.5-vl-3b-instruct | 2025-01-26 | 88876421.0 | |
| RedHatAI | Qwen2.5-VL-7B-Instruct-quantized.w8a8 | 2025-02-07 | 100186 | 11189 | w8a8 | 7.0 | qwen2.5-vl-7b-instruct | 2025-01-26 | 79189674.0 | |
| RedHatAI | Qwen2.5-VL-3B-Instruct-quantized.w8a8 | 2025-02-07 | 378762 | 121846 | w8a8 | 3.0 | qwen2.5-vl-3b-instruct | 2025-01-26 | 88876421.0 | |
| RedHatAI | Qwen2.5-VL-72B-Instruct-quantized.w8a8 | 2025-02-07 | 13105 | 285 | w8a8 | 72.0 | qwen2.5-vl-72b-instruct | 2025-01-27 | 6744401.0 | |
| RedHatAI | Qwen2.5-VL-72B-Instruct-quantized.w4a16 | 2025-02-07 | 12390 | 372 | w4a16 | 72.0 | qwen2.5-vl-72b-instruct | 2025-01-27 | 6744401.0 | |
| RedHatAI | pixtral-12b-quantized.w4a16 | 2025-02-08 | 35177 | 191 | w4a16 | 12.0 | pixtral-12b | 2024-09-11 | 105126.0 | |
| RedHatAI | pixtral-12b-quantized.w8a8 | 2025-02-08 | 4813 | 7 | w8a8 | 12.0 | pixtral-12b | 2024-09-11 | 105126.0 | |
| RedHatAI | Mixtral-8x22B-Instruct-v0.1-FP8 | 2025-02-26 | 12500 | 7330 | fp8 | 22.0 | mixtral-8x22b-instruct-v0.1 | 2024-04-16 | 6175042.0 | |
| RedHatAI | Mistral-Small-24B-Instruct-2501-quantized.w8a8 | 2025-03-03 | 126465 | 20069 | w8a8 | 24.0 | mistral-small-24b-instruct-2501 | 2025-01-28 | 7148090.0 | |
| RedHatAI | QwQ-32B-FP8-dynamic | 2025-03-05 | 24588 | 1035 | fp8 | 32.0 | qwq-32b | 2024-11-27 | 1432887.0 | |
| RedHatAI | Mixtral-8x7B-Instruct-v0.1-FP8 | 2025-03-06 | 154925 | 1432 | fp8 | 7.0 | mixtral-8x7b-instruct-v0.1 | 2023-12-10 | 22498540.0 | |
| RedHatAI | QwQ-32B-quantized.w8a8 | 2025-03-11 | 824 | 23 | w8a8 | 32.0 | qwq-32b | 2024-11-27 | 1432887.0 | |
| RedHatAI | Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic | 2025-03-27 | 393502 | 21492 | fp8 | 24.0 | mistral-small-3.1-24b-instruct-2503 | 2025-03-11 | 3534828.0 | |
| RedHatAI | Llama-4-Scout-17B-16E-Instruct-FP8-dynamic | 2025-04-10 | 213125 | 10381 | fp8 | 17.0 | llama-4-scout-17b-16e-instruct | 2025-04-02 | 6860901.0 | |
| RedHatAI | Mistral-Small-3.1-24B-Instruct-2503-quantized.w4a16 | 2025-04-15 | 457909 | 1972 | w4a16 | 24.0 | mistral-small-3.1-24b-instruct-2503 | 2025-03-11 | 3534828.0 | |
| RedHatAI | Mistral-Small-3.1-24B-Instruct-2503-quantized.w8a8 | 2025-04-15 | 33251 | 1316 | w8a8 | 24.0 | mistral-small-3.1-24b-instruct-2503 | 2025-03-11 | 3534828.0 | |
| RedHatAI | Qwen2.5-7B-Instruct-quantized.w4a16 | 2025-04-16 | 7909 | 385 | w4a16 | 7.0 | qwen2.5-7b-instruct | 2024-09-16 | 153771461.0 | |
| RedHatAI | Llama-4-Scout-17B-16E-Instruct-quantized.w4a16 | 2025-04-25 | 672385 | 5321 | w4a16 | 17.0 | llama-4-scout-17b-16e-instruct | 2025-04-02 | 6860901.0 | |
| RedHatAI | gemma-3-4b-it-FP8-dynamic | 2025-04-28 | 34382 | 1839 | fp8 | 4.0 | gemma-3-4b | 2025-02-20 | 22033988.0 | |
| RedHatAI | gemma-3-12b-it-FP8-dynamic | 2025-04-28 | 51688 | 3470 | fp8 | 12.0 | gemma-3-12b | 2025-03-01 | 20342230.0 | |
| RedHatAI | gemma-3-27b-it-FP8-dynamic | 2025-04-28 | 1016734 | 529481 | fp8 | 27.0 | gemma-3-27b | 2025-03-01 | 15588759.0 | |
| RedHatAI | Qwen3-4B-quantized.w4a16 | 2025-05-02 | 101987 | 15162 | w4a16 | 4.0 | qwen3-4b | 2025-04-27 | 66110120.0 | |
| RedHatAI | Qwen3-32B-FP8-dynamic | 2025-05-02 | 136755 | 1212 | fp8 | 32.0 | qwen3-32b | 2025-04-27 | 41610959.0 | |
| RedHatAI | Qwen3-0.6B-FP8-dynamic | 2025-05-02 | 24890 | 470 | fp8 | 0.6 | qwen3-0.6b | 2025-04-27 | 151103506.0 | |
| RedHatAI | Qwen3-8B-FP8-dynamic | 2025-05-02 | 277824 | 55601 | fp8 | 8.0 | qwen3-8b | 2025-04-27 | 86991025.0 | |
| RedHatAI | Qwen3-4B-FP8-dynamic | 2025-05-02 | 11203 | 5694 | fp8 | 4.0 | qwen3-4b | 2025-04-27 | 66110120.0 | |
| RedHatAI | Qwen3-14B-FP8-dynamic | 2025-05-02 | 11759 | 715 | fp8 | 14.0 | qwen3-14b | 2025-04-27 | 22293281.0 | |
| RedHatAI | Qwen3-1.7B-FP8-dynamic | 2025-05-02 | 15568 | 1826 | fp8 | 1.7 | qwen3-1.7b | 2025-04-27 | 48575630.0 | |
| RedHatAI | Qwen3-1.7B-quantized.w4a16 | 2025-05-02 | 26456 | 7122 | w4a16 | 1.7 | qwen3-1.7b | 2025-04-27 | 48575630.0 | |
| RedHatAI | Qwen3-0.6B-quantized.w4a16 | 2025-05-02 | 30257 | 3479 | w4a16 | 0.6 | qwen3-0.6b | 2025-04-27 | 151103506.0 | |
| RedHatAI | Qwen3-30B-A3B-FP8-dynamic | 2025-05-03 | 200203 | 5512 | fp8 | 30.0 | qwen3-30b-a3b | 2025-04-27 | 14822289.0 | |
| RedHatAI | Qwen3-235B-A22B-FP8-dynamic | 2025-05-04 | 30975 | 176 | fp8 | 235.0 | qwen3-235b-a22b | 2025-04-27 | 5732250.0 | |
| RedHatAI | Qwen3-32B-quantized.w4a16 | 2025-05-05 | 51353 | 567 | w4a16 | 32.0 | qwen3-32b | 2025-04-27 | 41610959.0 | |
| RedHatAI | Qwen3-14B-quantized.w4a16 | 2025-05-05 | 11107 | 821 | w4a16 | 14.0 | qwen3-14b | 2025-04-27 | 22293281.0 | |
| RedHatAI | Qwen3-8B-quantized.w4a16 | 2025-05-06 | 161042 | 33685 | w4a16 | 8.0 | qwen3-8b | 2025-04-27 | 86991025.0 | |
| RedHatAI | Qwen3-30B-A3B-quantized.w4a16 | 2025-05-06 | 81590 | 39946 | w4a16 | 30.0 | qwen3-30b-a3b | 2025-04-27 | 14822289.0 | |
| RedHatAI | Llama-4-Maverick-17B-128E-Instruct-FP8 | 2025-05-12 | 58841 | 15136 | fp8 | 17.0 | llama-4-maverick-17b-128e-instruct | 2025-04-01 | 676585.0 | |
| RedHatAI | gemma-3-1b-it-FP8-dynamic | 2025-05-16 | 7422 | 467 | fp8 | 1.0 | gemma-3-1b | 2025-03-10 | 41661191.0 | |
| RedHatAI | gemma-3-4b-it-quantized.w8a8 | 2025-06-04 | 5531 | 465 | w8a8 | 4.0 | gemma-3-4b | 2025-02-20 | 22033988.0 | |
| RedHatAI | gemma-3-4b-it-quantized.w4a16 | 2025-06-04 | 76554 | 6644 | w4a16 | 4.0 | gemma-3-4b | 2025-02-20 | 22033988.0 | |
| RedHatAI | gemma-3-12b-it-quantized.w8a8 | 2025-06-04 | 33222 | 1086 | w8a8 | 12.0 | gemma-3-12b | 2025-03-01 | 20342230.0 | |
| RedHatAI | gemma-3-12b-it-quantized.w4a16 | 2025-06-04 | 137393 | 38519 | w4a16 | 12.0 | gemma-3-12b | 2025-03-01 | 20342230.0 | |
| RedHatAI | gemma-3-27b-it-quantized.w8a8 | 2025-06-04 | 26211 | 772 | w8a8 | 27.0 | gemma-3-27b | 2025-03-01 | 15588759.0 | |
| RedHatAI | gemma-3-27b-it-quantized.w4a16 | 2025-06-04 | 947091 | 326056 | w4a16 | 27.0 | gemma-3-27b | 2025-03-01 | 15588759.0 | |
| RedHatAI | Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic | 2025-06-05 | 342 | 3 | fp8 | 8.0 | sparse-llama-3.1-8b-tldr-2of4 | 2025-05-16 | 60.0 | |
| RedHatAI | gemma-3-1b-it-quantized.w8a8 | 2025-06-05 | 88321 | 296 | w8a8 | 1.0 | gemma-3-1b | 2025-03-10 | 41661191.0 | |
| RedHatAI | gemma-3-1b-it-quantized.w4a16 | 2025-06-05 | 10556 | 11 | w4a16 | 1.0 | gemma-3-1b | 2025-03-10 | 41661191.0 | |
| RedHatAI | Llama-3.1-8B-tldr-FP8-dynamic | 2025-06-06 | 671 | 9 | fp8 | 8.0 | llama-3.1-8b-tldr | 2024-09-28 | 693.0 | |
| RedHatAI | Llama-4-Maverick-17B-128E-Instruct-quantized.w4a16 | 2025-06-12 | 23004 | 6439 | w4a16 | 17.0 | llama-4-maverick-17b-128e-instruct | 2025-04-01 | 676585.0 | |
| RedHatAI | Qwen3-30B-A3B-NVFP4 | 2025-06-17 | 190129 | 34470 | nvfp4 | 30.0 | qwen3-30b-a3b | 2025-04-27 | 14822289.0 | |
| RedHatAI | Mistral-Small-3.2-24B-Instruct-2506-FP8 | 2025-06-25 | 12880 | 354 | fp8 | 24.0 | mistral-small-3.2-24b-instruct-2506 | 2025-06-19 | 4750494.0 | |
| RedHatAI | Llama-3.1-70B-Instruct-NVFP4 | 2025-06-27 | 5042 | 499 | nvfp4 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| RedHatAI | Llama-3.1-70B-Instruct-NVFP4A16 | 2025-06-27 | 183 | 28 | nvfp4 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| RedHatAI | Qwen3-32B-NVFP4 | 2025-06-27 | 139899 | 3864 | nvfp4 | 32.0 | qwen3-32b | 2025-04-27 | 41610959.0 | |
| RedHatAI | Qwen3-32B-NVFP4A16 | 2025-06-27 | 36506 | 284 | nvfp4 | 32.0 | qwen3-32b | 2025-04-27 | 41610959.0 | |
| RedHatAI | Qwen3-0.6B-FP8-BLOCK | 2025-06-30 | 76383 | 16101 | fp8 | 0.6 | qwen3-0.6b | 2025-04-27 | 151103506.0 | |
| RedHatAI | Qwen3-30B-A3B-FP8-block | 2025-07-01 | 96205 | 20028 | fp8 | 30.0 | qwen3-30b-a3b | 2025-04-27 | 14822289.0 | |
| RedHatAI | watt-tool-8B-FP8-dynamic | 2025-07-02 | 21 | 6 | fp8 | 8.0 | watt-tool-8b | 2024-12-19 | 735844.0 | |
| RedHatAI | ToolACE-2-Llama-3.1-8B-FP8-dynamic | 2025-07-02 | 16 | 3 | fp8 | 8.0 | toolace-2-llama-3.1-8b | 2025-03-12 | 60721.0 | |
| RedHatAI | Llama-4-Scout-17B-16E-Instruct-NVFP4 | 2025-07-08 | 20466 | 899 | nvfp4 | 17.0 | llama-4-scout-17b-16e-instruct | 2025-04-02 | 6860901.0 | |
| RedHatAI | SmolLM3-3B-FP8-dynamic | 2025-07-28 | 6482 | 608 | fp8 | 3.0 | smollm3-3b | 2025-07-08 | 3939411.0 | |
| RedHatAI | SmolLM3-3B-quantized.w4a16 | 2025-07-31 | 946 | 28 | w4a16 | 3.0 | smollm3-3b | 2025-07-08 | 3939411.0 | |
| RedHatAI | gemma-3n-E4B-it-FP8-dynamic | 2025-08-01 | 161958 | 464 | fp8 | 4.0 | gemma-3n-e4b | 2025-06-03 | 1321114.0 | |
| RedHatAI | gemma-3n-E2B-it-FP8-dynamic | 2025-08-01 | 1760 | 118 | fp8 | 2.0 | gemma-3n-e2b | 2025-06-12 | 3091542.0 | |
| RedHatAI | gemma-3n-E4B-it-quantized.w8a8 | 2025-08-01 | 2246 | 19 | w8a8 | 4.0 | gemma-3n-e4b | 2025-06-03 | 1321114.0 | |
| RedHatAI | gemma-3n-E4B-it-quantized.w4a16 | 2025-08-01 | 1545 | 12 | w4a16 | 4.0 | gemma-3n-e4b | 2025-06-03 | 1321114.0 | |
| RedHatAI | gemma-3n-E2B-it-quantized.w8a8 | 2025-08-01 | 657 | 7 | w8a8 | 2.0 | gemma-3n-e2b | 2025-06-12 | 3091542.0 | |
| RedHatAI | gemma-3n-E2B-it-quantized.w4a16 | 2025-08-01 | 1200 | 9 | w4a16 | 2.0 | gemma-3n-e2b | 2025-06-12 | 3091542.0 | |
| RedHatAI | Llama-3.3-70B-Instruct-NVFP4 | 2025-08-05 | 26471 | 3201 | nvfp4 | 70.0 | llama-3.3-70b-instruct | 2024-11-26 | 12995295.0 | |
| RedHatAI | Voxtral-Mini-3B-2507-FP8-dynamic | 2025-08-21 | 18187 | 654 | fp8 | 3.0 | voxtral-mini-3b-2507 | 2025-07-01 | 5114685.0 | |
| RedHatAI | Qwen3-Coder-480B-A35B-Instruct-FP8 | 2025-09-04 | 1164 | 38 | fp8 | 480.0 | qwen3-coder-480b-a35b-instruct | 2025-07-22 | 983122.0 | |
| RedHatAI | Apertus-8B-Instruct-2509-FP8-dynamic | 2025-09-18 | 159849 | 1481 | fp8 | 8.0 | apertus-8b-instruct-2509 | 2025-08-13 | 2544082.0 | |
| RedHatAI | Apertus-70B-Instruct-2509-FP8-dynamic | 2025-09-18 | 2329 | 648 | fp8 | 70.0 | apertus-70b-instruct-2509 | 2025-09-01 | 331604.0 | |
| RedHatAI | Apertus-70B-Instruct-2509-quantized.w4a16 | 2025-09-21 | 178491 | 456 | w4a16 | 70.0 | apertus-70b-instruct-2509 | 2025-09-01 | 331604.0 | |
| RedHatAI | Voxtral-Small-24B-2507-FP8-dynamic | 2025-09-26 | 41857 | 7544 | fp8 | 24.0 | voxtral-small-24b-2507 | 2025-07-01 | 490950.0 | |
| RedHatAI | Qwen3-VL-235B-A22B-Instruct-FP8-dynamic | 2025-09-28 | 29624 | 31 | fp8 | 235.0 | qwen3-vl-235b-a22b-instruct | 2025-09-22 | 6694922.0 | |
| RedHatAI | Qwen3-VL-235B-A22B-Instruct-FP8-block | 2025-09-28 | 1209 | 8 | fp8 | 235.0 | qwen3-vl-235b-a22b-instruct | 2025-09-22 | 6694922.0 | |
| RedHatAI | NVIDIA-Nemotron-Nano-9B-v2-FP8-dynamic | 2025-09-30 | 33546 | 943 | fp8 | 9.0 | nvidia-nemotron-nano-9b-v2 | 2025-08-12 | 3323024.0 | |
| RedHatAI | Llama-3.1-8B-Instruct-FP8-block | 2025-10-10 | 724 | 7 | fp8 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| RedHatAI | Qwen3-14B-FP8-block | 2025-10-12 | 262 | 12 | fp8 | 14.0 | qwen3-14b | 2025-04-27 | 22293281.0 | |
| RedHatAI | Qwen3-32B-FP8-block | 2025-10-12 | 173 | 12 | fp8 | 32.0 | qwen3-32b | 2025-04-27 | 41610959.0 | |
| RedHatAI | Qwen3-8B-FP8-block | 2025-10-12 | 936 | 37 | fp8 | 8.0 | qwen3-8b | 2025-04-27 | 86991025.0 | |
| RedHatAI | Qwen3-VL-235B-A22B-Instruct-NVFP4 | 2025-10-14 | 55941 | 6995 | nvfp4 | 235.0 | qwen3-vl-235b-a22b-instruct | 2025-09-22 | 6694922.0 | |
| RedHatAI | Llama-3.3-70B-Instruct-FP8-block | 2025-10-20 | 25802 | 1777 | fp8 | 70.0 | llama-3.3-70b-instruct | 2024-11-26 | 12995295.0 | |
| RedHatAI | NVIDIA-Nemotron-Nano-9B-v2-quantized.w4a16 | 2025-10-22 | 8310 | 448 | w4a16 | 9.0 | nvidia-nemotron-nano-9b-v2 | 2025-08-12 | 3323024.0 | |
| RedHatAI | Llama-4-Scout-17B-16E-Instruct-FP8-block | 2025-10-23 | 724 | 5 | fp8 | 17.0 | llama-4-scout-17b-16e-instruct | 2025-04-02 | 6860901.0 | |
| RedHatAI | Llama-3.1-8B-Instruct-NVFP4 | 2025-10-23 | 128133 | 20212 | nvfp4 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| RedHatAI | Qwen3-14B-NVFP4 | 2025-10-23 | 75169 | 4034 | nvfp4 | 14.0 | qwen3-14b | 2025-04-27 | 22293281.0 | |
| RedHatAI | Qwen3-8B-NVFP4 | 2025-10-23 | 27837 | 3339 | nvfp4 | 8.0 | qwen3-8b | 2025-04-27 | 86991025.0 | |
| RedHatAI | Mistral-Small-3.2-24B-Instruct-2506-NVFP4 | 2025-10-23 | 91426 | 5047 | nvfp4 | 24.0 | mistral-small-3.2-24b-instruct-2506 | 2025-06-19 | 4750494.0 | |
| RedHatAI | Llama-4-Maverick-17B-128E-Instruct-FP8-block | 2025-10-27 | 139 | 7 | fp8 | 17.0 | llama-4-maverick-17b-128e-instruct | 2025-04-01 | 676585.0 | |
| RedHatAI | Llama-4-Maverick-17B-128E-Instruct-NVFP4 | 2025-10-27 | 10007 | 903 | nvfp4 | 17.0 | llama-4-maverick-17b-128e-instruct | 2025-04-01 | 676585.0 | |
| RedHatAI | Qwen3-235B-A22B-NVFP4 | 2025-10-29 | 1330 | 265 | nvfp4 | 235.0 | qwen3-235b-a22b | 2025-04-27 | 5732250.0 | |
| RedHatAI | Qwen3-235B-A22B-Instruct-2507-NVFP4 | 2025-10-29 | 12920 | 7067 | nvfp4 | 235.0 | qwen3-235b-a22b-instruct-2507 | 2025-07-21 | 1480496.0 | |
| RedHatAI | Qwen3-4B-Thinking-2507-quantized.w4a16 | 2025-12-05 | 1223 | 39 | w4a16 | 4.0 | qwen3-4b-thinking-2507 | 2025-08-05 | 6455777.0 | |
| RedHatAI | Qwen3-4B-Instruct-2507-quantized.w4a16 | 2025-12-05 | 1374 | 580 | w4a16 | 4.0 | qwen3-4b-instruct-2507 | 2025-08-05 | 52535540.0 | |
| RedHatAI | Qwen3-30B-A3B-Thinking-2507-quantized.w4a16 | 2025-12-05 | 839 | 272 | w4a16 | 30.0 | qwen3-30b-a3b-thinking-2507 | 2025-07-29 | 3751954.0 | |
| RedHatAI | Qwen3-30B-A3B-Instruct-2507-quantized.w4a16 | 2025-12-05 | 8164 | 1916 | w4a16 | 30.0 | qwen3-30b-a3b-instruct-2507 | 2025-07-28 | 13426532.0 | |
| RedHatAI | Qwen3-VL-32B-Instruct-FP8-dynamic | 2025-12-10 | 2404 | 651 | fp8 | 32.0 | qwen3-vl-32b-instruct | 2025-10-19 | 13645955.0 | |
| RedHatAI | Qwen3-VL-32B-Instruct-FP8-block | 2025-12-10 | 108 | 3 | fp8 | 32.0 | qwen3-vl-32b-instruct | 2025-10-19 | 13645955.0 | |
| RedHatAI | Qwen3-VL-32B-Instruct-NVFP4 | 2025-12-10 | 38004 | 10720 | nvfp4 | 32.0 | qwen3-vl-32b-instruct | 2025-10-19 | 13645955.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Instruct-NVFP4 | 2025-12-12 | 79125 | 4767 | nvfp4 | 80.0 | qwen3-next-80b-a3b-instruct | 2025-09-09 | 15688008.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Instruct-FP8-dynamic | 2025-12-12 | 214 | 8 | fp8 | 80.0 | qwen3-next-80b-a3b-instruct | 2025-09-09 | 15688008.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Instruct-FP8-block | 2025-12-12 | 87 | 9 | fp8 | 80.0 | qwen3-next-80b-a3b-instruct | 2025-09-09 | 15688008.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Instruct-quantized.w4a16 | 2025-12-12 | 2886 | 399 | w4a16 | 80.0 | qwen3-next-80b-a3b-instruct | 2025-09-09 | 15688008.0 | |
| RedHatAI | Mistral-Large-3-675B-Instruct-2512-NVFP4 | 2025-12-23 | 100 | 6 | nvfp4 | 675.0 | mistral-large-3-675b-instruct-2512 | 2025-11-28 | 9123.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 | 2025-12-23 | 9190 | 1061 | fp8 | 30.0 | nvidia-nemotron-3-nano-30b-a3b | 2025-12-03 | 462372.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Instruct-FP8 | 2025-12-24 | 5713 | 4719 | fp8 | 80.0 | qwen3-next-80b-a3b-instruct | 2025-09-09 | 15688008.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Thinking-quantized.w4a16 | 2025-12-24 | 572 | 42 | w4a16 | 80.0 | qwen3-next-80b-a3b-thinking | 2025-09-09 | 2386070.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Thinking-FP8-block | 2025-12-24 | 138 | 9 | fp8 | 80.0 | qwen3-next-80b-a3b-thinking | 2025-09-09 | 2386070.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Thinking-FP8-dynamic | 2025-12-24 | 1087 | 10 | fp8 | 80.0 | qwen3-next-80b-a3b-thinking | 2025-09-09 | 2386070.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Thinking-NVFP4 | 2025-12-24 | 1740 | 505 | nvfp4 | 80.0 | qwen3-next-80b-a3b-thinking | 2025-09-09 | 2386070.0 | |
| RedHatAI | Qwen3-4B-Instruct-2507-quantized.w8a8 | 2026-01-21 | 1830 | 310 | w8a8 | 4.0 | qwen3-4b-instruct-2507 | 2025-08-05 | 52535540.0 | |
| RedHatAI | Qwen3-4B-Thinking-2507-quantized.w8a8 | 2026-01-21 | 342 | 17 | w8a8 | 4.0 | qwen3-4b-thinking-2507 | 2025-08-05 | 6455777.0 | |
| RedHatAI | Qwen3-30B-A3B-Thinking-2507-quantized.w8a8 | 2026-01-21 | 537 | 453 | w8a8 | 30.0 | qwen3-30b-a3b-thinking-2507 | 2025-07-29 | 3751954.0 | |
| RedHatAI | Qwen3-30B-A3B-Instruct-2507-quantized.w8a8 | 2026-01-21 | 4247 | 3207 | w8a8 | 30.0 | qwen3-30b-a3b-instruct-2507 | 2025-07-28 | 13426532.0 | |
| RedHatAI | Ministral-3-14B-Instruct-2512-FP8-dynamic | 2026-01-23 | 1749 | 241 | fp8 | 14.0 | ministral-3-14b-instruct-2512 | 2025-10-31 | 1593707.0 | |
| RedHatAI | Ministral-3-14B-Instruct-2512-NVFP4 | 2026-02-04 | 1343 | 674 | nvfp4 | 14.0 | ministral-3-14b-instruct-2512 | 2025-10-31 | 1593707.0 | |
| RedHatAI | Llama-Guard-4-12B-FP8-dynamic | 2026-02-09 | 511 | 58 | fp8 | 12.0 | llama-guard-4-12b | 2025-04-23 | 1181909.0 | |
| RedHatAI | Llama-Guard-4-12B-quantized.w8a8 | 2026-02-09 | 198 | 14 | w8a8 | 12.0 | llama-guard-4-12b | 2025-04-23 | 1181909.0 | |
| RedHatAI | Llama-Guard-4-12B-quantized.w4a16 | 2026-02-16 | 83462 | 82836 | w4a16 | 12.0 | llama-guard-4-12b | 2025-04-23 | 1181909.0 | |
| RedHatAI | Qwen3.5-397B-A17B-FP8-dynamic | 2026-02-18 | 11825 | 4681 | fp8 | 397.0 | qwen3.5-397b-a17b | 2026-02-16 | 4628491.0 | |
| RedHatAI | Qwen3-Next-80B-A3B-Instruct-quantized.w8a8 | 2026-03-03 | 455 | 333 | w8a8 | 80.0 | qwen3-next-80b-a3b-instruct | 2025-09-09 | 15688008.0 | |
| RedHatAI | Qwen3.5-122B-A10B-FP8-dynamic | 2026-03-03 | 7561 | 1642 | fp8 | 122.0 | qwen3.5-122b-a10b | 2026-02-24 | 3830314.0 | |
| RedHatAI | Qwen3.5-35B-A3B-FP8-dynamic | 2026-03-07 | 9248 | 2118 | fp8 | 35.0 | qwen3.5-35b-a3b | 2026-02-24 | 13190350.0 | |
| RedHatAI | sarvam-30b-FP8-dynamic | 2026-03-09 | 135784 | 90938 | fp8 | 30.0 | sarvam-30b | 2026-03-03 | 228432.0 | |
| RedHatAI | sarvam-105b-FP8-dynamic | 2026-03-09 | 985 | 27 | fp8 | 105.0 | sarvam-105b | 2026-03-03 | 114315.0 | |
| RedHatAI | Qwen3.5-122B-A10B-NVFP4 | 2026-03-17 | 569591 | 268849 | nvfp4 | 122.0 | qwen3.5-122b-a10b | 2026-02-24 | 3830314.0 | |
| RedHatAI | Qwen3-235B-A22B-Instruct-2507-quantized.w8a8 | 2026-03-18 | 242 | 36 | w8a8 | 235.0 | qwen3-235b-a22b-instruct-2507 | 2025-07-21 | 1480496.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Super-120B-A12B-FP8 | 2026-03-26 | 5146 | 502 | fp8 | 120.0 | nvidia-nemotron-3-super-120b-a12b | 2026-03-06 | 9119.0 | |
| RedHatAI | Mistral-Small-4-119B-2603-NVFP4 | 2026-03-27 | 154 | 15 | nvfp4 | 119.0 | mistral-small-4-119b-2603 | 2026-01-23 | 357383.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | 2026-03-27 | 11315 | 2049 | nvfp4 | 120.0 | nvidia-nemotron-3-super-120b-a12b | 2026-03-06 | 9119.0 | |
| RedHatAI | gemma-4-26B-A4B-it-FP8-Dynamic | 2026-04-06 | 2714454 | 1999313 | fp8 | 26.0 | gemma-4-26b-a4b | 2026-03-11 | 34957706.0 | |
| RedHatAI | Qwen3.5-4B-quantized.w8a8 | 2026-04-15 | 21256 | 15558 | w8a8 | 4.0 | qwen3.5-4b | 2026-02-27 | 27389353.0 | |
| RedHatAI | Qwen3.5-4B-quantized.w4a16 | 2026-04-16 | 6737 | 5310 | w4a16 | 4.0 | qwen3.5-4b | 2026-02-27 | 27389353.0 | |
| RedHatAI | Qwen3.5-9B-quantized.w8a8 | 2026-04-16 | 18930 | 13380 | w8a8 | 9.0 | qwen3.5-9b | 2026-02-27 | 32952516.0 | |
| RedHatAI | Qwen3.5-9B-quantized.w4a16 | 2026-04-24 | 15325 | 6011 | w4a16 | 9.0 | qwen3.5-9b | 2026-02-27 | 32952516.0 | |
| RedHatAI | Qwen3.6-35B-A3B-FP8 | 2026-05-01 | 26054 | 13287 | fp8 | 35.0 | qwen3.6-35b-a3b | 2026-04-15 | 15661179.0 | |
| RedHatAI | Qwen3.6-27B-FP8 | 2026-05-01 | 54984 | 34378 | fp8 | 27.0 | qwen3.6-27b | 2026-04-21 | 12695526.0 | |
| RedHatAI | Qwen3.5-4B-FP8-dynamic | 2026-05-11 | 29503 | 10601 | fp8 | 4.0 | qwen3.5-4b | 2026-02-27 | 27389353.0 | |
| RedHatAI | Qwen3.5-9B-FP8-dynamic | 2026-05-11 | 313156 | 179418 | fp8 | 9.0 | qwen3.5-9b | 2026-02-27 | 32952516.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Super-120B-A12B-FP8-Dflash | 2026-06-01 | 138 | 129 | fp8 | 120.0 | nvidia-nemotron-3-super-120b-a12b | 2026-03-06 | 9119.0 | |
| RedHatAI | Mellum2-12B-A2.5B-Thinking-FP8-Dynamic | 2026-06-02 | 37 | 37 | fp8 | 12.0 | mellum2-12b-a2.5b-thinking | 2026-05-26 | 29935.0 | |
| RedHatAI | Mellum2-12B-A2.5B-Thinking-NVFP4 | 2026-06-03 | 290 | 277 | nvfp4 | 12.0 | mellum2-12b-a2.5b-thinking | 2026-05-26 | 29935.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-dynamic | 2026-06-04 | 14765 | 14467 | fp8 | 550.0 | nvidia-nemotron-3-ultra-550b-a55b | 2026-05-26 | 1328.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Ultra-550B-A55B-FP8-block | 2026-06-04 | 7881 | 7851 | fp8 | 550.0 | nvidia-nemotron-3-ultra-550b-a55b | 2026-05-26 | 1328.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Ultra-550B-A55B-quantized.w4a16 | 2026-06-04 | 2444 | 1730 | w4a16 | 550.0 | nvidia-nemotron-3-ultra-550b-a55b | 2026-05-26 | 1328.0 | |
| RedHatAI | gemma-4-12B-it-FP8-Dynamic | 2026-06-08 | 26193 | 26193 | fp8 | 12.0 | gemma-4-12b | 2026-05-23 | 3395580.0 | |
| RedHatAI | diffusiongemma-26B-A4B-it-NVFP4 | 2026-06-09 | 606150 | 606150 | nvfp4 | 26.0 | diffusiongemma-26b-a4b | 2026-06-09 | 1783259.0 | |
| RedHatAI | NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 | 2026-06-09 | 48 | 48 | nvfp4 | 550.0 | nvidia-nemotron-3-ultra-550b-a55b | 2026-05-26 | 1328.0 | |
| nvidia | Qwen3.6-27B-NVFP4 | 2026-06-22 | 538687 | 538687 | nvfp4 | 27.0 | qwen3.6-27b | 2026-04-21 | 12695526.0 | |
| nvidia | Qwen3.6-35B-A3B-NVFP4 | 2026-05-27 | 8492943 | 7114686 | nvfp4 | 35.0 | qwen3.6-35b-a3b | 2026-04-15 | 15661179.0 | |
| nvidia | Mistral-Medium-3.5-128B-NVFP4 | 2026-06-30 | 9273 | 9273 | nvfp4 | 128.0 | mistral-medium-3.5-128b | 2026-03-31 | 767229.0 | |
| nvidia | NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 | 2026-06-24 | 47 | 47 | nvfp4 | 75.0 | nvidia-nemotron-labs-3-puzzle-75b-a9b | 2026-06-24 | 159.0 | |
| nvidia | NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 | 2026-06-03 | 527337 | 462747 | nvfp4 | 550.0 | nvidia-nemotron-3-ultra-550b-a55b | 2026-05-26 | 1328.0 | |
| nvidia | NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 | 2026-03-10 | 5547346 | 1314206 | nvfp4 | 120.0 | nvidia-nemotron-3-super-120b-a12b | 2026-03-06 | 9119.0 | |
| nvidia | Gemma-4-26B-A4B-NVFP4 | 2026-05-01 | 3751276 | 2167115 | nvfp4 | 26.0 | gemma-4-26b-a4b | 2026-03-11 | 34957706.0 | |
| nvidia | Qwen3.5-122B-A10B-NVFP4 | 2026-05-13 | 93897 | 89426 | nvfp4 | 122.0 | qwen3.5-122b-a10b | 2026-02-24 | 3830314.0 | |
| nvidia | Gemma-4-31B-IT-NVFP4 | 2026-04-02 | 6634757 | 1644077 | nvfp4 | 31.0 | gemma-4-31b | 2026-03-11 | 33431223.0 | |
| nvidia | diffusiongemma-26B-A4B-it-NVFP4 | 2026-06-10 | 1687519 | 1687519 | nvfp4 | 26.0 | diffusiongemma-26b-a4b | 2026-06-09 | 1783259.0 | |
| nvidia | NVIDIA-Nemotron-3-Nano-4B-GGUF | 2026-03-07 | 69030 | 17539 | gguf | 4.0 | nvidia-nemotron-3-nano-4b | 2025-05-03 | 186207.0 | |
| nvidia | Qwen3.5-397B-A17B-NVFP4-V2 | 2026-06-29 | 19319 | 19319 | nvfp4 | 397.0 | qwen3.5-397b-a17b | 2026-02-16 | 4628491.0 | |
| nvidia | NVIDIA-Nemotron-3-Super-120B-A12B-FP8 | 2026-03-10 | 2232273 | 249979 | fp8 | 120.0 | nvidia-nemotron-3-super-120b-a12b | 2026-03-06 | 9119.0 | |
| nvidia | NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-FP8 | 2026-06-24 | 43 | 43 | fp8 | 75.0 | nvidia-nemotron-labs-3-puzzle-75b-a9b | 2026-06-24 | 159.0 | |
| nvidia | Llama-3.3-70B-Instruct-NVFP4 | 2025-01-16 | 588720 | 49272 | nvfp4 | 70.0 | llama-3.3-70b-instruct | 2024-11-26 | 12995295.0 | |
| nvidia | Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4 | 2026-04-24 | 3887330 | 2310420 | nvfp4 | 30.0 | nemotron-3-nano-omni-30b-a3b-reasoning | 2026-04-20 | 1650159.0 | |
| nvidia | Llama-3.1-8B-Instruct-NVFP4 | 2025-09-05 | 1031863 | 279780 | nvfp4 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| nvidia | Qwen3-Next-80B-A3B-Thinking-NVFP4 | 2025-12-11 | 210291 | 2868 | nvfp4 | 80.0 | qwen3-next-80b-a3b-thinking | 2025-09-09 | 2386070.0 | |
| nvidia | NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 | 2025-12-20 | 3720048 | 1447033 | nvfp4 | 30.0 | nvidia-nemotron-3-nano-30b-a3b | 2025-12-03 | 462372.0 | |
| nvidia | Qwen3-Coder-480B-A35B-Instruct-NVFP4 | 2026-01-15 | 6334 | 992 | nvfp4 | 480.0 | qwen3-coder-480b-a35b-instruct | 2025-07-22 | 983122.0 | |
| nvidia | NVIDIA-Nemotron-3-Nano-4B-FP8 | 2026-03-12 | 96283 | 36474 | fp8 | 4.0 | nvidia-nemotron-3-nano-4b | 2025-05-03 | 186207.0 | |
| nvidia | Qwen3-VL-235B-A22B-Instruct-NVFP4-MLPerf-Inference-Closed-V6.1-FP8-KV | 2026-06-15 | 18869 | 18869 | fp8 | 235.0 | qwen3-vl-235b-a22b-instruct | 2025-09-22 | 6694922.0 | |
| nvidia | Llama-3.1-8B-Instruct-FP8 | 2024-08-29 | 2313871 | 220917 | fp8 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| nvidia | Llama-3.1-70B-Instruct-FP8 | 2024-08-29 | 119577 | 3481 | fp8 | 70.0 | llama-3.1-70b-instruct | 2024-07-16 | 23145855.0 | |
| nvidia | Llama-3.1-405B-Instruct-FP8 | 2024-08-29 | 106076 | 4683 | fp8 | 405.0 | llama-3.1-405b-instruct | 2024-07-16 | 4205284.0 | |
| nvidia | Mistral-Nemo-12B-Instruct-ONNX-INT4 | 2024-11-13 | 0 | 0 | int4 | 12.0 | mistral-nemo-12b-instruct | 2024-07-18 | 18989.0 | |
| nvidia | Gemma-2b-it-ONNX-INT4 | 2024-11-14 | 0 | 0 | int4 | 2.0 | gemma-2b | 2024-02-08 | 4753699.0 | |
| nvidia | Meta-Llama-3.1-8B-Instruct-ONNX-INT4 | 2024-11-15 | 2031 | 12 | int4 | 8.0 | llama-3.1-8b-instruct | 2024-07-18 | 167356088.0 | |
| nvidia | Meta-Llama-3.2-3B-Instruct-ONNX-INT4 | 2024-11-15 | 0 | 0 | int4 | 3.0 | llama-3.2-3b-instruct | 2024-09-18 | 45159369.0 | |
| nvidia | Mistral-7B-Instruct-v0.3-ONNX-INT4 | 2024-11-15 | 0 | 0 | int4 | 7.0 | mistral-7b-instruct-v0.3 | 2024-05-22 | 37068277.0 | |
| nvidia | Nemotron-Mini-4B-Instruct-ONNX-INT4 | 2024-11-15 | 0 | 0 | int4 | 4.0 | nemotron-mini-4b-instruct | 2024-09-10 | 1091720.0 | |
| nvidia | Llama-3.1-8B-Medusa-FP8 | 2025-01-13 | 1383 | 14 | fp8 | 8.0 | llama-3.1-8b-medusa | 2025-02-08 | 40.0 | |
| nvidia | Llama-3.1-405B-Instruct-NVFP4 | 2025-01-16 | 63233 | 1848 | nvfp4 | 405.0 | llama-3.1-405b-instruct | 2024-07-16 | 4205284.0 | |
| nvidia | Llama-4-Scout-17B-16E-Instruct-NVFP4 | 2025-04-14 | 584350 | 82819 | nvfp4 | 17.0 | llama-4-scout-17b-16e-instruct | 2025-04-02 | 6860901.0 | |
| nvidia | Llama-4-Maverick-17B-128E-Instruct-FP8 | 2025-04-14 | 15402 | 536 | fp8 | 17.0 | llama-4-maverick-17b-128e-instruct | 2025-04-01 | 676585.0 | |
| nvidia | Llama-4-Scout-17B-16E-Instruct-FP8 | 2025-04-14 | 1977775 | 337468 | fp8 | 17.0 | llama-4-scout-17b-16e-instruct | 2025-04-02 | 6860901.0 | |
| nvidia | Llama-3_1-Nemotron-Ultra-253B-v1-FP8 | 2025-04-30 | 31145 | 466 | fp8 | 253.0 | llama-3-1-nemotron-ultra-253b-v1 | 2025-04-07 | 611335.0 | |
| nvidia | Llama-3.3-70B-Instruct-FP8 | 2025-05-05 | 652385 | 44380 | fp8 | 70.0 | llama-3.3-70b-instruct | 2024-11-26 | 12995295.0 | |
| nvidia | Llama-3_3-Nemotron-Super-49B-v1-FP8 | 2025-05-13 | 36617 | 1091 | fp8 | 49.0 | llama-3-3-nemotron-super-49b-v1 | 2025-03-16 | 995109.0 | |
| nvidia | Nemotron-H-47B-Reasoning-128K-FP8 | 2025-05-29 | 5957 | 96 | fp8 | 47.0 | nemotron-h-47b-reasoning-128k | 2025-05-22 | 12027.0 | |
| nvidia | Nemotron-H-8B-Reasoning-128K-FP8 | 2025-06-05 | 3454 | 110 | fp8 | 8.0 | nemotron-h-8b-reasoning-128k | 2025-06-05 | 115726.0 | |
| nvidia | Qwen3-235B-A22B-FP8 | 2025-07-08 | 11453 | 204 | fp8 | 235.0 | qwen3-235b-a22b | 2025-04-27 | 5732250.0 | |
| nvidia | Qwen3-235B-A22B-NVFP4 | 2025-07-08 | 86858 | 6040 | nvfp4 | 235.0 | qwen3-235b-a22b | 2025-04-27 | 5732250.0 | |
| nvidia | Qwen3-30B-A3B-NVFP4 | 2025-07-08 | 705026 | 77705 | nvfp4 | 30.0 | qwen3-30b-a3b | 2025-04-27 | 14822289.0 | |
| nvidia | Llama-3_3-Nemotron-Super-49B-v1_5-FP8 | 2025-07-31 | 662762 | 236173 | fp8 | 49.0 | llama-3-3-nemotron-super-49b-v1-5 | 2025-07-25 | 1924492.0 | |
| nvidia | Qwen3-8B-NVFP4 | 2025-09-09 | 444588 | 151863 | nvfp4 | 8.0 | qwen3-8b | 2025-04-27 | 86991025.0 | |
| nvidia | Qwen3-8B-FP8 | 2025-09-09 | 93143 | 4306 | fp8 | 8.0 | qwen3-8b | 2025-04-27 | 86991025.0 | |
| nvidia | Qwen3-14B-NVFP4 | 2025-09-09 | 549490 | 74501 | nvfp4 | 14.0 | qwen3-14b | 2025-04-27 | 22293281.0 | |
| nvidia | Qwen3-14B-FP8 | 2025-09-09 | 48112 | 6524 | fp8 | 14.0 | qwen3-14b | 2025-04-27 | 22293281.0 | |
| nvidia | Qwen3-32B-NVFP4 | 2025-09-09 | 627209 | 248135 | nvfp4 | 32.0 | qwen3-32b | 2025-04-27 | 41610959.0 | |
| nvidia | Qwen2.5-VL-7B-Instruct-FP8 | 2025-09-10 | 11667 | 982 | fp8 | 7.0 | qwen2.5-vl-7b-instruct | 2025-01-26 | 79189674.0 | |
| nvidia | Qwen2.5-VL-7B-Instruct-NVFP4 | 2025-09-10 | 392160 | 8092 | nvfp4 | 7.0 | qwen2.5-vl-7b-instruct | 2025-01-26 | 79189674.0 | |
| nvidia | NVIDIA-Nemotron-Nano-9B-v2-FP8 | 2025-09-22 | 873510 | 231553 | fp8 | 9.0 | nvidia-nemotron-nano-9b-v2 | 2025-08-12 | 3323024.0 | |
| nvidia | Llama-3.1-Nemotron-Nano-VL-8B-V1-FP4-QAD | 2025-10-01 | 4834 | 652 | fp4-qad | 8.0 | llama-3.1-nemotron-nano-vl-8b-v1 | 2025-06-03 | 12958380.0 | |
| nvidia | NVIDIA-Nemotron-Nano-9B-v2-NVFP4 | 2025-10-07 | 100660 | 19510 | nvfp4 | 9.0 | nvidia-nemotron-nano-9b-v2 | 2025-08-12 | 3323024.0 | |
| nvidia | NVIDIA-Nemotron-Nano-12B-v2-VL-FP8 | 2025-10-22 | 1199421 | 28469 | fp8 | 12.0 | nvidia-nemotron-nano-12b-v2-vl | 2025-10-21 | 1067576.0 | |
| nvidia | NVIDIA-Nemotron-Nano-12B-v2-VL-NVFP4-QAD | 2025-10-22 | 113897 | 27341 | nvfp4 | 12.0 | nvidia-nemotron-nano-12b-v2-vl | 2025-10-21 | 1067576.0 | |
| nvidia | Llama-3_3-Nemotron-Super-49B-v1_5-NVFP4 | 2025-11-11 | 49609 | 7144 | nvfp4 | 49.0 | llama-3-3-nemotron-super-49b-v1-5 | 2025-07-25 | 1924492.0 | |
| nvidia | NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 | 2025-12-06 | 5902087 | 349655 | fp8 | 30.0 | nvidia-nemotron-3-nano-30b-a3b | 2025-12-03 | 462372.0 | |
| nvidia | Qwen3-Next-80B-A3B-Instruct-NVFP4 | 2025-12-09 | 218134 | 46637 | nvfp4 | 80.0 | qwen3-next-80b-a3b-instruct | 2025-09-09 | 15688008.0 | |
| nvidia | Qwen3-235B-A22B-Thinking-2507-FP4-Eagle3 | 2025-12-15 | 260 | 51 | fp4-eagle3 | 235.0 | qwen3-235b-a22b-thinking-2507 | 2025-07-25 | 591612.0 | |
| nvidia | Qwen3-VL-235B-A22B-Instruct-NVFP4 | 2025-12-25 | 16244 | 4228 | nvfp4 | 235.0 | qwen3-vl-235b-a22b-instruct | 2025-09-22 | 6694922.0 | |
| nvidia | Qwen3-235B-A22B-Thinking-2507-NVFP4 | 2025-12-30 | 27542 | 1535 | nvfp4 | 235.0 | qwen3-235b-a22b-thinking-2507 | 2025-07-25 | 591612.0 | |
| nvidia | Qwen3-235B-A22B-Instruct-2507-NVFP4 | 2025-12-30 | 25296 | 3615 | nvfp4 | 235.0 | qwen3-235b-a22b-instruct-2507 | 2025-07-21 | 1480496.0 | |
| nvidia | Qwen3-VL-235B-A22B-Instruct-NVFP4-MLPerf-Inference-Closed-V6.0 | 2026-01-27 | 190025 | 106913 | unknown | 235.0 | qwen3-vl-235b-a22b-instruct | 2025-09-22 | 6694922.0 | |
| nvidia | Qwen3.5-397B-A17B-NVFP4 | 2026-02-16 | 2429467 | 480127 | nvfp4 | 397.0 | qwen3.5-397b-a17b | 2026-02-16 | 4628491.0 | |
| nvidia | NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-FP8 | 2026-04-01 | 4890 | 396 | fp8 | 30.0 | nvidia-nemotron-labs-3-elastic-30b-a3b | 2026-04-01 | 5468.0 | |
| nvidia | Qwen3-VL-235B-A22B-Instruct-NVFP4-MLPerf-Inference-Closed-V6.1 | 2026-04-07 | 6053 | 5989 | unknown | 235.0 | qwen3-vl-235b-a22b-instruct | 2025-09-22 | 6694922.0 | |
| nvidia | NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-NVFP4 | 2026-04-14 | 6732 | 992 | nvfp4 | 30.0 | nvidia-nemotron-labs-3-elastic-30b-a3b | 2026-04-01 | 5468.0 | |
| nvidia | Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 | 2026-04-24 | 313431 | 111509 | fp8 | 30.0 | nemotron-3-nano-omni-30b-a3b-reasoning | 2026-04-20 | 1650159.0 | |
| nvidia | Wan2.2-T2V-A14B-Diffusers-NVFP4 | 2026-05-13 | 0 | 0 | nvfp4 | 14.0 | wan2.2-t2v-a14b-diffusers | 2025-07-28 | 1588952.0 | |
| nvidia | Wan2.2-T2V-A14B-Diffusers-FP8 | 2026-05-13 | 0 | 0 | fp8 | 14.0 | wan2.2-t2v-a14b-diffusers | 2025-07-28 | 1588952.0 | |
| nvidia | llama-nemotron-embed-vl-1b-v2-fp8 | 2026-05-14 | 6039 | 5948 | fp8 | 1.0 | llama-nemotron-embed-vl-1b-v2 | 2025-12-03 | 639215.0 |
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