metadata

license: apache-2.0
datasets:
  - cerebras/SlimPajama-627B
  - bigcode/starcoderdata
  - HuggingFaceH4/ultrachat_200k
  - HuggingFaceH4/ultrafeedback_binarized
language:
  - en
widget:
  - example_title: Fibonacci (Python)
    messages:
      - role: system
        content: You are a chatbot who can help code!
      - role: user
        content: >-
          Write me a function to calculate the first 10 digits of the fibonacci
          sequence in Python and print it out to the CLI.
tags:
  - mlx
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

mlx-community-staging/TinyLlama-1.1B-Chat-v1.0-mlx-4Bit

The Model mlx-community-staging/TinyLlama-1.1B-Chat-v1.0-mlx-4Bit was converted to MLX format from TinyLlama/TinyLlama-1.1B-Chat-v1.0 using mlx-lm version 0.24.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community-staging/TinyLlama-1.1B-Chat-v1.0-mlx-4Bit")

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)