license, datasets, language, base_model, pipeline_tag, tags

license	datasets	language	base_model	pipeline_tag	tags
apache-2.0	PrimeIntellect/fineweb-edu PrimeIntellect/fineweb PrimeIntellect/StackV1-popular mlfoundations/dclm-baseline-1.0-parquet open-web-math/open-web-math arcee-ai/EvolKit-75K arcee-ai/Llama-405B-Logits arcee-ai/The-Tomb mlabonne/open-perfectblend-fixed microsoft/orca-agentinstruct-1M-v1-cleaned Post-training-Data-Flywheel/AutoIF-instruct-61k-with-funcs Team-ACE/ToolACE Synthia-coder ServiceNow-AI/M2Lingual AI-MO/NuminaMath-TIR allenai/tulu-3-sft-personas-code allenai/tulu-3-sft-personas-math allenai/tulu-3-sft-personas-math-grade allenai/tulu-3-sft-personas-algebra	en	PrimeIntellect/INTELLECT-1-Instruct	text-generation	mlx

mlx-community/INTELLECT-1-Instruct-bf16

The Model mlx-community/INTELLECT-1-Instruct-bf16 was converted to MLX format from PrimeIntellect/INTELLECT-1-Instruct using mlx-lm version 0.20.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/INTELLECT-1-Instruct-bf16")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)