README.md

metadata

library_name: transformers
tags:
  - mlx
  - mlx-my-repo
base_model: eleenakidangenb2b/gemma-Manager-Coaching-Finetune-test

Scotto2025/gemma-Manager-Coaching-Finetune-test-mlx-8Bit

The Model Scotto2025/gemma-Manager-Coaching-Finetune-test-mlx-8Bit was converted to MLX format from eleenakidangenb2b/gemma-Manager-Coaching-Finetune-test using mlx-lm version 0.22.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Scotto2025/gemma-Manager-Coaching-Finetune-test-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)