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)