xLAM-2-fc-r

This repo provides the GGUF format for the xLAM-2-3b-fc-r model. Here's a link to original model xLAM-2-3b-fc-r.

Large Action Models (LAMs) are advanced language models designed to enhance decision-making by translating user intentions into executable actions. As the brains of AI agents, LAMs autonomously plan and execute tasks to achieve specific goals, making them invaluable for automating workflows across diverse domains. This model release is for research purposes only.

The new xLAM-2 series, built on our most advanced data synthesis, processing, and training pipelines, marks a significant leap in multi-turn conversation and tool usage. Trained using our novel APIGen-MT framework, which generates high-quality training data through simulated agent-human interactions. Our models achieve state-of-the-art performance on BFCL and τ-bench benchmarks, outperforming frontier models like GPT-4o and Claude 3.5. Notably, even our smaller models demonstrate superior capabilities in multi-turn scenarios while maintaining exceptional consistency across trials.

We've also refined the chat template and vLLM integration, making it easier to build advanced AI agents. Compared to previous xLAM models, xLAM-2 offers superior performance and seamless deployment across applications.

Table of Contents

• Model Series
• Using GGUF Files
• Benchmark Results
• Citation

Model Series

xLAM series are significantly better at many things including general tasks and function calling. For the same number of parameters, the model have been fine-tuned across a wide range of agent tasks and scenarios, all while preserving the capabilities of the original model.

*Note: The default context length for Qwen-2.5-based models is 32k, but you can use techniques like YaRN (Yet Another Recursive Network) to achieve maximum 128k context length. Please refer to here for more details.

You can also explore our previous xLAM series here.

The -fc suffix indicates that the models are fine-tuned for function calling tasks, while the -r suffix signifies a research release.

✅ All models are fully compatible with vLLM and Transformers-based inference frameworks.

Using GGUF Files

For scenarios requiring more efficient inference or deployment on resource-constrained devices, we provide GGUF versions of our models, which are compatible with llama.cpp and similar frameworks.

How to Download GGUF Files

1. Install Hugging Face CLI:

pip install huggingface-hub

2. Login to Hugging Face:

huggingface-cli login

3. Download the GGUF model:

huggingface-cli download Salesforce/Llama-xLAM-2-8b-fc-r-gguf Llama-xLAM-2-8b-fc-r-gguf --local-dir . --local-dir-use-symlinks False

Prompt Template

The GGUF models use the following prompt template:

{TASK_INSTRUCTION}
You have access to a set of tools. When using tools, make calls in a single JSON array:

[{"name": "tool_call_name", "arguments": {"arg1": "value1", "arg2": "value2"}}, ... (additional parallel tool calls as needed)]

If no tool is suitable, state that explicitly. If the user's input lacks required parameters, ask for clarification. Do not interpret or respond until tool results are returned. Once they are available, process them or make additional calls if needed. For tasks that don't require tools, such as casual conversation or general advice, respond directly in plain text. The available tools are:

{AVAILABLE_TOOLS}

{USER_QUERY}assistant

{ASSISTANT_QUERY}user

{USER_QUERY}assistant

Usage Examples

Command Line

1. Install llama.cpp framework from the source here
2. Run the inference task as shown below. For configuration of generation-related parameters, refer to llama.cpp documentation

llama-cli -m [PATH-TO-LOCAL-GGUF]

Python Framework

1. Install [llama-cpp-python](https://github.com/abetlen/llama-cpp-