🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
- nlp
- natural-language-processing
- pytorch
- pytorch-transformers
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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4.1, MiniMax-H3, Gemma 4, FLUX and more.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).
GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
Swap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.
Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!
Efficient Triton Kernels for LLM Training
TranslatedFirefly: a large model training tool that supports training large models such as Qwen2.5, Qwen2, Yi1.5, Phi-3, Llama3, Gemma, MiniCPM, Yi, Deepseek, Orion, Xverse, Mixtral-8x7B, Zephyr, Mistral, Baichuan2, Llma2, Llama, Qwen, Baichuan, ChatGLM2, InternLM, Ziya2, Vicuna, Bloom, etc.
Gemma open-weight LLM library, from Google DeepMind
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
Implementing LLM architectures from scratch in PyTorch
🔥🔥 The extremely fast inference engine for decision model
TranslatedMultimodal retrieval in practice: intent recognition, prompt engineering, Qwen LoRA, EmbeddingGemma 2, and InfoNCE contrastive learning. Includes real demos, integration of your own data, training and evaluation, and a learning guide to RAG retrieval components.
Transform Git history: rewrite, clean, and optimize your repositories with ease.