---
base_model: Salesforce/xLAM-7b-fc-r
datasets:
- Salesforce/xlam-function-calling-60k
language:
- en
license: cc-by-nc-4.0
pipeline_tag: text-generation
tags:
- function-calling
- LLM Agent
- tool-use
- deepseek
- pytorch
- mlx
extra_gated_heading: Acknowledge to follow corresponding license to access the repository
extra_gated_button_content: Agree and access repository
extra_gated_fields:
  First Name: text
  Last Name: text
  Country: country
  Affiliation: text
---

# andstor/xLAM-7b-fc-r-mlx

The Model [andstor/xLAM-7b-fc-r-mlx](https://huggingface.co/andstor/xLAM-7b-fc-r-mlx) was converted to MLX format from [Salesforce/xLAM-7b-fc-r](https://huggingface.co/Salesforce/xLAM-7b-fc-r) using mlx-lm version **0.19.1**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("andstor/xLAM-7b-fc-r-mlx")

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)
```