README.md

metadata

base_model: princeton-nlp/gemma-2-9b-it-SimPO
tags:
  - alignment-handbook
  - generated_from_trainer
  - mlx
datasets:
  - princeton-nlp/gemma2-ultrafeedback-armorm
license: mit
pipeline_tag: text-generation
library_name: mlx
model-index:
  - name: princeton-nlp/gemma-2-9b-it-SimPO
    results: []

mlx-community/gemma-2-9b-it-SimPO-8bit

This model mlx-community/gemma-2-9b-it-SimPO-8bit was converted to MLX format from princeton-nlp/gemma-2-9b-it-SimPO using mlx-lm version 0.26.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/gemma-2-9b-it-SimPO-8bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)