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README.md
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- [Benchmark Results](#benchmark-results)
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- [Citation](#citation)
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## Model Series
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***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](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct#processing-long-texts) for more details.
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## Usage
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### Framework versions
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If you use our model or dataset in your work, please cite our paper:
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```bibtex
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@article{
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title={APIGen-MT: Agentic
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author={Prabhakar, Akshara and Liu, Zuxin and
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journal={arXiv preprint arXiv:2504.03601},
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year={2025}
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}
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```
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Additionally, please check our other related works regarding xLAM and consider citing them as well:
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```bibtex
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@article{zhang2025actionstudio,
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title={ActionStudio: A Lightweight Framework for Data and Training of Action Models},
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journal={arXiv preprint arXiv:2409.03215},
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year={2024}
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}
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```
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```bibtex
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@article{liu2024apigen,
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- [Benchmark Results](#benchmark-results)
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- [Citation](#citation)
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---
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## Model Series
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[xLAM](https://huggingface.co/collections/Salesforce/xlam-models-65f00e2a0a63bbcd1c2dade4) series are significant better at many things including general tasks and function calling.
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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.
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| Model | # Total Params | Context Length |Release Date | Category | Download Model | Download GGUF files |
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|------------------------|----------------|------------|-------------|-------|----------------|----------|
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| Llama-xLAM-2-70b-fc-r | 70B | 128k | Mar. 26, 2025 | Multi-turn Conversation, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/Llama-xLAM-2-70b-fc-r) | NA |
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| Llama-xLAM-2-8b-fc-r | 8B | 128k | Mar. 26, 2025 | Multi-turn Conversation, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r) | [🤗 Link](https://huggingface.co/Salesforce/Llama-xLAM-2-8b-fc-r-gguf) |
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| xLAM-2-32b-fc-r | 32B | 32k (max 128k)* | Mar. 26, 2025 | Multi-turn Conversation, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-2-32b-fc-r) | NA |
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| xLAM-2-3b-fc-r | 3B | 32k (max 128k)* | Mar. 26, 2025 | Multi-turn Conversation, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-2-3b-fc-r) | [🤗 Link](https://huggingface.co/Salesforce/xLAM-2-3b-fc-r-gguf) |
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| xLAM-2-1b-fc-r | 1B | 32k (max 128k)* | Mar. 26, 2025 | Multi-turn Conversation, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-2-1b-fc-r) | [🤗 Link](https://huggingface.co/Salesforce/xLAM-2-1b-fc-r-gguf) |
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| xLAM-7b-r | 7.24B | 32k | Sep. 5, 2024|General, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-7b-r) | -- |
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| xLAM-8x7b-r | 46.7B | 32k | Sep. 5, 2024|General, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-8x7b-r) | -- |
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| xLAM-8x22b-r | 141B | 64k | Sep. 5, 2024|General, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-8x22b-r) | -- |
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| xLAM-1b-fc-r | 1.35B | 16k | July 17, 2024 | Function-calling| [🤗 Link](https://huggingface.co/Salesforce/xLAM-1b-fc-r) | [🤗 Link](https://huggingface.co/Salesforce/xLAM-1b-fc-r-gguf) |
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| xLAM-7b-fc-r | 6.91B | 4k | July 17, 2024| Function-calling| [🤗 Link](https://huggingface.co/Salesforce/xLAM-7b-fc-r) | [🤗 Link](https://huggingface.co/Salesforce/xLAM-7b-fc-r-gguf) |
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| xLAM-v0.1-r | 46.7B | 32k | Mar. 18, 2024 |General, Function-calling | [🤗 Link](https://huggingface.co/Salesforce/xLAM-v0.1-r) | -- |
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***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](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct#processing-long-texts) for more details.
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### 📦 Model Naming Conventions
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- `xLAM-7b-r`: A general-purpose v1.0 or v2.0 release of the **Large Action Model**, fine-tuned for broad agentic capabilities. The `-r` suffix indicates it is a **research** release.
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- `xLAM-7b-fc-r`: A specialized variant where `-fc` denotes fine-tuning for **function calling** tasks, also marked for **research** use.
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- ✅ All models are fully compatible with VLLM, FastChat, and Transformers-based inference frameworks.
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---
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## Usage
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### Framework versions
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If you use our model or dataset in your work, please cite our paper:
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```bibtex
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@article{prabhakar2025apigen,
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title={APIGen-MT: Agentic PIpeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay},
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author={Prabhakar, Akshara and Liu, Zuxin and Zhu, Ming and Zhang, Jianguo and Awalgaonkar, Tulika and Wang, Shiyu and Liu, Zhiwei and Chen, Haolin and Hoang, Thai and others},
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journal={arXiv preprint arXiv:2504.03601},
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year={2025}
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}
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```
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```bibtex
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@article{zhang2025actionstudio,
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title={ActionStudio: A Lightweight Framework for Data and Training of Action Models},
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journal={arXiv preprint arXiv:2409.03215},
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year={2024}
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}
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```
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Additionally, please check our other related works regarding xLAM and consider citing them as well:
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```bibtex
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@article{liu2024apigen,
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