MLX
Safetensors
llama
How to use from the
Use from the
MLX library
# Download the model from the Hub
pip install huggingface_hub[hf_xet]

hf download mlx-community/AMD-Llama-135m-code-float16 --local-dir AMD-Llama-135m-code-float16

mlx-community/AMD-Llama-135m-code-float16

The Model mlx-community/AMD-Llama-135m-code-float16 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-float16")

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)
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Safetensors
Model size
0.1B params
Tensor type
F16
ยท
MLX
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