MLX
Safetensors
llama
4-bit precision
AMD-Llama-135m-4bit / README.md
Goekdeniz-Guelmez's picture
a43a3f8367eb57cfdbd51b4a7fee15d1e531f2ee71b2d752a3557ffd19b59aae
b9ea494 verified
|
raw
history blame
900 Bytes
---
base_model: amd/AMD-Llama-135m
datasets:
- cerebras/SlimPajama-627B
- manu/project_gutenberg
license: apache-2.0
tags:
- mlx
---
# mlx-community/AMD-Llama-135m-4bit
The Model [mlx-community/AMD-Llama-135m-4bit](https://huggingface.co/mlx-community/AMD-Llama-135m-4bit) was converted to MLX format from [amd/AMD-Llama-135m](https://huggingface.co/amd/AMD-Llama-135m) using mlx-lm version **0.18.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/AMD-Llama-135m-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)
```