metadata

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

mlx-community/xLAM-7b-fc-r

The Model mlx-community /xLAM-7b-fc-r was converted to MLX format from Salesforce/xLAM-7b-fc-r using mlx-lm version 0.19.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

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

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)