README.md

metadata

base_model: ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025
datasets:
  - collaiborateorg/BioMedData
library_name: transformers
license: other
tags:
  - generated_from_trainer
  - medical
  - Healthcare & Lifesciences
  - BioMed
  - chain-of-thought
  - mlx
thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
model-index:
  - name: Bio-Medical-Llama-3-2-1B-CoT-012025
    results: []

mlx-community/Bio-Medical-Llama-3-2-1B-CoT-012025

The Model mlx-community/Bio-Medical-Llama-3-2-1B-CoT-012025 was converted to MLX format from ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025 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/Bio-Medical-Llama-3-2-1B-CoT-012025")

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)