README.md

metadata

license: apache-2.0
datasets:
  - 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
language:
  - en
base_model: PrimeIntellect/INTELLECT-1-Instruct
pipeline_tag: text-generation
tags:
  - mlx

mlx-community/INTELLECT-1-Instruct-8bit

The Model mlx-community/INTELLECT-1-Instruct-8bit 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-8bit")

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)