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# requires sentence_transformers>=3.2.0
from sentence_transformers import SentenceTransformer, export_optimized_onnx_model, export_dynamic_quantized_onnx_model

# The model to export to ONNX (+ optimize, quantize), OpenVINO
model_id = "mixedbread-ai/mxbai-embed-large-v1"
# Where to save the exported models locally
output_dir = model_id.replace("/", "-")

onnx_model = SentenceTransformer(model_id, backend="onnx", model_kwargs={"export": True})
onnx_model.save_pretrained(output_dir)

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Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

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#VERBOSE=0 torchrun --nproc_per_node 3 self_contained_pp_LOC.py
import os, random, numpy as np, torch, torch.nn as nn, torch.distributed as dist, torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, DistributedSampler
from datasets import load_dataset
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

STEP, local_rank, world_size, verbose = 0, int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), os.environ.get("VERBOSE", "0") == "1"

def set_all_seed(seed):

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"""
A minimal, fast example generating text with Llama 3.1 in MLX.

To run, install the requirements:

    pip install -U mlx transformers fire

Then generate text with:

    python l3min.py "How tall is K2?"

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MLX LM with the OpenAI Python Package

1. Install

Install MLX LM and openai:

pip install mlx-lm openai

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MLX LM with the OpenAI Python Package

1. Install

Install MLX LM and openai:

pip install mlx-lm openai

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Clone the model

This can take a little while:

# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install

git clone https://huggingface.co/black-forest-labs/FLUX.1-schnell

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// Used in https://jina.ai/tokenizer (Aug. 14th version)
// Define variables for magic numbers
const MAX_HEADING_LENGTH = 6;
const MAX_HEADING_CONTENT_LENGTH = 200;
const MAX_HEADING_UNDERLINE_LENGTH = 200;
const MAX_HTML_HEADING_ATTRIBUTES_LENGTH = 100;
const MAX_LIST_ITEM_LENGTH = 200;
const MAX_NESTED_LIST_ITEMS = 5;
const MAX_LIST_INDENT_SPACES = 7;
const MAX_BLOCKQUOTE_LINE_LENGTH = 200;

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from great_tables import GT, md, html, system_fonts
import pandas as pd

power_cie_prepared_tbl = pd.read_csv("./data/2023_cie_power_cons.csv")

# Create a Great Tables object
ciep_gt_tbl = GT(data=power_cie_prepared_tbl)

# Apply wider color ranges & formatting
gt_tbl = ciep_gt_tbl \