Code and documentation to train Stanford's Alpaca models, and generate the data.
- deep-learning
- instruction-following
- language-model
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Code and documentation to train Stanford's Alpaca models, and generate the data.
TranslatedChinese LLaMA & Alpaca LLMs + local CPU/GPU training and deployment
Instruct-tune LLaMA on consumer hardware
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
Locally run an Instruction-Tuned Chat-Style LLM
TranslatedChinese LLaMA-2 & Alpaca-2 LLMs with 64K long context models
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.
Chinese-Vicuna: A Chinese Instruction-following LLaMA-based Model —— 一个中文低资源的llama+lora方案,结构参考alpaca
Open-source multimodal retrieval engine (Morphik Core)
Together Mixture-Of-Agents (MoA) – 65.1% on AlpacaEval with OSS models
Infinity is a high-throughput, low-latency serving engine for text-embeddings, reranking models, clip, clap and colpali
The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts to initiate any meaningful PR on this repo and integrate as many LLM related technologies as possible. 我们打造了方便研究人员上手和使用大模型等微调平台,我们欢迎开源爱好者发起任何有意义的pr!
Code and documents of LongLoRA and LongAlpaca (ICLR 2024 Oral)
mPLUG-Owl: The Powerful Multi-modal Large Language Model Family