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.
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
tags:
- mlx
library_name: mlx
Kimang18/tinyllama-1.1B-Chat-v1.0-mlx-4bit
This model Kimang18/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 0.28.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Kimang18/tinyllama-1.1B-Chat-v1.0-mlx-4bit")
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)