---
license: cc-by-nc-4.0
datasets:
- Salesforce/APIGen-MT-5k
- Salesforce/xlam-function-calling-60k
language:
- en
pipeline_tag: text-generation
tags:
- function-calling
- LLM Agent
- tool-use
- llama
- qwen
- pytorch
- LLaMA-factory
- mlx
- mlx-my-repo
library_name: transformers
base_model: Salesforce/Llama-xLAM-2-8b-fc-r
---

# mxmcc/Llama-xLAM-2-8b-fc-r-mlx-8Bit

The Model [mxmcc/Llama-xLAM-2-8b-fc-r-mlx-8Bit](https://huggingface.co/mxmcc/Llama-xLAM-2-8b-fc-r-mlx-8Bit) was converted to MLX format from [Salesforce/Llama-xLAM-2-8b-fc-r](https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r) using mlx-lm version **0.22.3**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("mxmcc/Llama-xLAM-2-8b-fc-r-mlx-8Bit")

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)
```