README.md

metadata

license: llama3.1
base_model: cognitivecomputations/dolphin-2.9.4-llama3.1-8b
tags:
  - generated_from_trainer
  - mlx
datasets:
  - cognitivecomputations/Dolphin-2.9
  - m-a-p/CodeFeedback-Filtered-Instruction
  - cognitivecomputations/dolphin-coder
  - cognitivecomputations/samantha-data
  - microsoft/orca-math-word-problems-200k
  - mlabonne/FineTome-100k
  - arcee/agent_data
  - PawanKrd/math-gpt-4o-200k
  - cognitivecomputations/SystemChat-2.0

Felprot75/dolphin-2.9.4-llama3.1-8b-Q8-mlx

The Model Felprot75/dolphin-2.9.4-llama3.1-8b-Q8-mlx was converted to MLX format from cognitivecomputations/dolphin-2.9.4-llama3.1-8b using mlx-lm version 0.19.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Felprot75/dolphin-2.9.4-llama3.1-8b-Q8-mlx")

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)