Welcome to the xLAM model family! Large Action Models (LAMs) are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications. We provide a series of xLAMs in different sizes to cater to various applications, including those optimized for function-calling and general agent applications: The `fc` series of models are optimized for function-calling capability, providing fast, accurate, and structured responses based on input queries and available APIs. These models are fine-tuned based on the deepseek-coder models and are designed to be small enough for deployment on personal devices like phones or computers.

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xLAM-1b-fc-r-gguf

Salesforce

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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xLAM-2-1b-fc-r

Salesforce

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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xLAM-2-1b-fc-r-gguf

Salesforce

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

텍스트 생성

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요금 미확인멀티모달 및 채널 요금 별도 확인

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xLAM-2-3b-fc-r

Salesforce

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

텍스트 생성

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

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xLAM-2-3b-fc-r-gguf

Salesforce

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.

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