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.
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
Universal LLM Deployment Engine with ML Compilation
The official repo of Qwen (通义千问) chat & pretrained large language model proposed by Alibaba Cloud.
Ongoing research training transformer models at scale
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
An open source implementation of CLIP.
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
Hierarchical Reasoning Model Official Release
An open-source, tool-augmented conversational language model from Fudan University
The official GitHub page for the survey paper "A Survey of Large Language Models".
A PyTorch-based Speech Toolkit
Pocket Flow: 100-line LLM framework. Let Agents build Agents!
🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading
[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型
A collection of libraries to optimise AI model performances
Open-source offline translation library written in Python
Plug and Play Language Model implementation. Allows to steer topic and attributes of GPT-2 models.
A clinical decision-evidence benchmark: pull the one fact an answer rests on, and see whether the assistant's action moves with the evidence. Built on HealthBench.
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
BlueBERT, pre-trained on PubMed abstracts and clinical notes (MIMIC-III).
[CVPR 2025] Video Narration as Vocabulary & Video as Long Document