alvarobartt

from huggingface_hub import get_safetensors_metadata

model_id = "deepseek-ai/DeepSeek-R1"
dtype_bytes = {"F32": 4, "F16": 2, "F8": 1}

metadata = get_safetensors_metadata(model_id)
memory = (
    sum(count * dtype_bytes[key.split("_")[0]] for key, count in metadata.parameter_count.items())
    / (1024**3)
    * 1.18

alvarobartt

from huggingface_hub import get_safetensors_metadata

model_id = "mistralai/Mistral-7B-Instruct-v0.1"
precision = "F8"
dtype_bytes = {"F32": 4, "F16": 2, "BF16": 2, "F8": 1, "INT8": 1, "INT4": 0.5}

metadata = get_safetensors_metadata(model_id)
memory = ((sum(metadata.parameter_count.values()) * dtype_bytes[precision]) / (1024**3)) * 1.18
print(f"{model_id=} requires {memory=}GB")

alvarobartt

alvarobartt

import duckdb
from datasets import Dataset

# Create DuckDB connection
con = duckdb.connect()
con.execute("INSTALL httpfs;")
con.execute("LOAD httpfs;")

# Query the dataset
query = """

alvarobartt

import torch
from diffusers import DiffusionPipeline

model_id = "black-forest-labs/FLUX.1-dev"
adapter_id = "alvarobartt/ghibli-characters-flux-lora"

pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipeline.load_lora_weights(adapter_id)
pipeline.to("cuda")

alvarobartt

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Define the model name
model_name = "HuggingFaceTB/SmolLM-1.7B-Instruct"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)

alvarobartt

from transformers import AutoConfig

if __name__ == "__main__":
    config = AutoConfig.from_pretrained("meta-llama/Llama-3.1-8B-Instruct", token="hf_...")

    tokens_in_cache = 1024  # this is the only arg that will change over time (as more requests are sent)
    precision_in_bytes = 2  # float16 or bfloat16

    cache_size_bytes = (
        2 * 

alvarobartt

import time
from typing import Any, Dict, Literal

from distilabel.llms import vLLM
from distilabel.llms.typing import ChatType
from distilabel.pipeline import Pipeline
from distilabel.steps import LoadDataFromDicts
from distilabel.steps.tasks.prometheus_eval import PrometheusEval

_CUSTOM_RUBRICS = {

alvarobartt

# pip install "distilabel[vllm]>=1.1.1"
# pip install flash-attn --no-build-isolation
# huggingface-cli login
import time

from distilabel.llms import vLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import KeepColumns, LoadHubDataset
from distilabel.steps.tasks import PrometheusEval

alvarobartt

from distilabel.llms import (
    AnthropicLLM,
    InferenceEndpointsLLM,
    OpenAILLM,
)
from distilabel.pipeline import Pipeline
from distilabel.steps import (
    CombineColumns,
    KeepColumns,
    LoadDataFromDicts,