metadata
base_model: amd/AMD-Llama-135m-code
datasets:
- cerebras/SlimPajama-627B
- manu/project_gutenberg
license: apache-2.0
tags:
- mlx
mlx-community/AMD-Llama-135m-code-bfloat16
The Model mlx-community/AMD-Llama-135m-code-bfloat16 was converted to MLX format from amd/AMD-Llama-135m-code using mlx-lm version 0.18.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/AMD-Llama-135m-code-bfloat16")
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)