This model was generated using llama.cpp at commit 6adc3c3e. xLAM series are significant 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. 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. — Transformers 4.46.1 (or later) — PyTorch 2.5.1+cu124 (or later) — Datasets 3.1.0 (or later) — Tokenizers 0.20.3 (or later) The new xLAM models are designed to work seamlessly with the Hugging Face Transformers library and utilize natural chat templates for an easy and intuitive conversational experience. Below are examples of how to use these models. The xLAM models can also be efficiently served using vLLM for high-throughput inference. Please use vllm>=0.6.5 since earlier versions will cause degraded performance for Qwen-based models. Here’s a minimal example to test tool usage with the served endpoint: For more advanced…
Модальности:
Генерация текста
Области применения:
Диалог / чат Вызов функций (Tool use)
Задача: Генерация текста
Автор: Mungert
Теги: gguf, function-calling, LLM Agent, tool-use, llama, qwen, LLaMA-factory, en
Лайков: 4 | Загрузок: 368
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
