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  • 1
    CogVLM

    CogVLM

    A state-of-the-art open visual language model

    CogVLM is an open-source visual–language model suite—and its GUI-oriented sibling CogAgent—aimed at image understanding, grounding, and multi-turn dialogue, with optional agent actions on real UI screenshots. The flagship CogVLM-17B combines ~10B visual parameters with ~7B language parameters and supports 490×490 inputs; CogAgent-18B extends this to 1120×1120 and adds plan/next-action outputs plus grounded operation coordinates for GUI tasks. The repo provides multiple ways to run models (CLI, web demo, and OpenAI-Vision–style APIs), along with quantization options that reduce VRAM needs (e.g., 4-bit). It includes checkpoints for chat, base, and grounding variants, plus recipes for model-parallel inference and LoRA fine-tuning. ...
    Downloads: 3 This Week
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  • 2
    Chinese-LLaMA-Alpaca 2

    Chinese-LLaMA-Alpaca 2

    Chinese LLaMA-2 & Alpaca-2 Large Model Phase II Project

    This project is developed based on the commercially available large model Llama-2 released by Meta. It is the second phase of the Chinese LLaMA&Alpaca large model project. The Chinese LLaMA-2 base model and the Alpaca-2 instruction fine-tuning large model are open-sourced. These models expand and optimize the Chinese vocabulary on the basis of the original Llama-2, use large-scale Chinese data for incremental pre-training, and further improve the basic semantics and command understanding of Chinese. ...
    Downloads: 2 This Week
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  • 3
    LLaVA

    LLaVA

    Visual Instruction Tuning: Large Language-and-Vision Assistant

    Visual instruction tuning towards large language and vision models with GPT-4 level capabilities.
    Downloads: 2 This Week
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  • 4
    Bard API

    Bard API

    The unofficial python package that returns response of Google Bard

    ...It has been designed with a lightweight structure that can easily adapt to the emergence of an official API. Therefore, I strongly discourage using it for any other purposes. If you have access to official PaLM-2 API, replace the provided response with the corresponding official code.
    Downloads: 2 This Week
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  • 5
    Ailice

    Ailice

    AIlice is a fully autonomous, general-purpose AI agent

    AIlice is an open-source autonomous AI agent framework built to function as a general-purpose assistant that can plan, decompose, and execute complex tasks through a structured multi-agent architecture. The project presents itself as a standalone assistant powered by open-source language models, with an internal design that treats user requests almost like executable programs rather than simple chat prompts. Its core IACT architecture allows the system to break large goals into smaller sub-tasks, assign them to dynamically created agents, and combine the results with a focus on resilience and fault tolerance. ...
    Downloads: 0 This Week
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  • 6
    Chinese Llama 2 7B

    Chinese Llama 2 7B

    The first Chinese LLaMA2 model in the open source community

    Chinese Llama 2 7B is an open-source large language model adapted from the LLaMA-2 architecture and optimized for Chinese and bilingual Chinese-English applications. The project provides a version of LLaMA-2 that has been further trained on Chinese data so it can better understand and generate text in Chinese while maintaining compatibility with the original model ecosystem.
    Downloads: 0 This Week
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  • 7
    llama2-webui

    llama2-webui

    Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere

    Running Llama 2 with gradio web UI on GPU or CPU from anywhere (Linux/Windows/Mac).
    Downloads: 0 This Week
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  • 8
    Chinese-LLaMA-Alpaca-2 v2.0

    Chinese-LLaMA-Alpaca-2 v2.0

    Chinese LLaMA & Alpaca large language model + local CPU/GPU training

    This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which...
    Downloads: 0 This Week
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  • 9
    Repo of Tree of Thoughts (ToT)

    Repo of Tree of Thoughts (ToT)

    Implementation of "Tree of Thoughts

    Language models are increasingly being deployed for general problem-solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of...
    Downloads: 0 This Week
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  • 10
    LM Human Preferences

    LM Human Preferences

    Code for the paper Fine-Tuning Language Models from Human Preferences

    ...The repository includes scripts to train the reward model (learning to rank or score pairs of outputs), and to fine-tune a policy (a language model) with reinforcement learning (or related techniques) guided by that reward model. The code is provided “as is” and explicitly says it may no longer run out-of-the-box due to dependencies or dataset migrations. It was tested on the smallest GPT-2 (124M parameters) under a specific environment (TensorFlow 1.x, specific CUDA / cuDNN combinations). It includes utilities for launching experiments, sampling from policies, and simple experiment orchestration.
    Downloads: 0 This Week
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  • 11
    GPT Neo

    GPT Neo

    An implementation of model parallel GPT-2 and GPT-3-style models

    ...This, as well as the fact that many GPUs became available to us, among other things, prompted us to move development over to GPT-NeoX. All evaluations were done using our evaluation harness. Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness.
    Downloads: 3 This Week
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