license: apache-2.0 language:

• en
• ro base_model: nightmedia/Medra4b-abliterated-q8-mlx datasets:
• drwlf/medra-thinking-768 tags:
• text-generation
• medical-ai
• summarization
• diagnostic-reasoning
• gemma-3
• fine-tuned
• mlx
• mlx
• mlx-my-repo model_size: 4B version: Medra v1 – Gemma Edition format: GGUF (Q4, Q8, BF16) author: Dr. Alexandru Lupoi & @nicoboss pipeline_tag: text-generation library_name: mlx

introvoyz041/Medra4b-abliterated-q8-mlx-mlx-8Bit

The Model introvoyz041/Medra4b-abliterated-q8-mlx-mlx-8Bit was converted to MLX format from nightmedia/Medra4b-abliterated-q8-mlx using mlx-lm version 0.28.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("introvoyz041/Medra4b-abliterated-q8-mlx-mlx-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)