LLM Models

We opensource our Aquila2 series, now including Aquila2 , the base language models, namely Aquila2-7B and Aquila2-34B , as well as AquilaChat2 , the chat models, namely AquilaChat2-7B and AquilaChat2-34B , as well as the long-text chat models, namely AquilaChat2-7B-16k and Aquila…

We introduce DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSe…

A small ~110M parameter language model implementing the DeepSeek-V4 architecture from scratch. This is the pretrained base model — see HuggingFaceTB/nanowhale-100m for the SFT/chat version.

DeepSeek-V2.5-1210 is an upgraded version of DeepSeek-V2.5, with improvements across various capabilities:

SingGuard-NSFA: Extensible Guardrails for Agentic AI via Generative Reasoning and Real-Time Classification

Llama-2-7B-32K-Instruct is an open-source, long-context chat model finetuned from Llama-2-7B-32K, over high-quality instruction and chat data. We built Llama-2-7B-32K-Instruct with less than 200 lines of Python script using Together API, and we also make the recipe fully availabl…

Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari

1. Model Summary 2. Use 3. Limitations 4. Training 5. Evaluation 7. Citation

0. TL;DR 1. Model Details 2. Training Details 3. Usage 4. Evaluation 5. Citation

--- license: mit library name: transformers ---

🦉GitHub 💬WeChat 百川API支持搜索增强和192K长窗口，新增百川搜索增强知识库、限时免费！ 🚀 百川大模型在线对话平台 已正式向公众开放 🎉

Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the Meta Llama 3.1 models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities. We u…