LMFlow
chatgpt_macro_for_texstudio
LMFlow | chatgpt_macro_for_texstudio | |
---|---|---|
10 | 2 | |
8,042 | 24 | |
3.5% | - | |
9.6 | 6.5 | |
5 days ago | 4 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LMFlow
- Your weekly machine learning digest
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Any guide/intro to fine-tuning anywhere?
You might want to have a look at LMFlow.
- Robin V2 Launches: Achieves Unparalleled Performance on OpenLLM!
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[D] Have you tried fine-tuning an open source LLM?
I'd like to recommend LMFlow (https://github.com/OptimalScale/LMFlow), a fast and extensible toolkit for finetuning and inference of large foundation models.
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[R] DetGPT: Detect What You Need via Reasoning
The "reasoning-based object detection" is a challenging problem because the detector needs to understand and reason about the user's coarse-grained/abstract instructions and analyze the current visual information to locate the target object accurately. In this direction, researchers from the Hong Kong University of Science and Technology and the University of Hong Kong have conducted some preliminary explorations. Specifically, they use a pre-trained visual encoder (BLIP-2) to extract visual features from images and align the visual features to the text space using an alignment function. They use a large-scale language model (Robin/Vicuna) to understand the user's question, combined with the visual information they see, to reason about the objects that users are truly interested in. Then, they provide the object names to the pre-trained detector (Grounding-DINO) for specific location prediction. In this way, the model can analyze the image based on any user instructions and accurately predict the location of the object of interest to the user. It is worth noting that the difficulty here mainly lies in the fact that the model needs to achieve task-specific output formats for different specific tasks as much as possible without damaging the model's original abilities. To guide the language model to follow specific patterns and generate outputs that conform to the object detection format, the research team used ChatGPT to generate cross-modal instruction data to fine-tune the model. Specifically, based on 5000 coco images, they used ChatGPT to create a 30,000 cross-modal image-text fine-tuning dataset. To improve the efficiency of training, they fixed other model parameters and only learned cross-modal linear mapping. Experimental results show that even if only the linear layer is fine-tuned, the language model can understand fine-grained image features and follow specific patterns to perform inference-based image detection tasks, showing excellent performance. This research topic has great potential. Based on this technology, the field of home robots will further shine: people in homes can use abstract or coarse-grained voice instructions to make robots understand, recognize, and locate the objects they need, and provide relevant services. In the field of industrial robots, this technology will bring endless vitality: industrial robots can cooperate more naturally with human workers, accurately understand their instructions and needs, and achieve intelligent decision-making and operations. On the production line, human workers can use coarse-grained voice instructions or text input to allow robots to automatically understand, recognize, and locate the items that need to be processed, thereby improving production efficiency and quality. With object detection models that come with reasoning capabilities, we can develop more intelligent, natural, and efficient robots to provide more convenient, efficient, and humane services to humans. This is a field with broad prospects and deserves more attention and further exploration by more researchers. DetGPT supports multiple language models and has been validated based on two language models, Robin-13B and Vicuna-13B. The Robin series language model is a dialogue model trained by the LMFlow team ( https://github.com/OptimalScale/LMFlow) at the Hong Kong University of Science and Technology, achieving results competitive to Vicuna on multiple language ability evaluation benchmarks (model download: https://github.com/OptimalScale/LMFlow#model-zoo). Previously, the LMFlow team trained a vertical GPT model using a consumer-grade 3090 graphics card in just 5 hours. Today, this team, in collaboration with the NLP Group at the University of Hong Kong, has brought us a multimodal surprise. Welcome to try our demo and open-source code! Online demo: https://detgpt.github.io/ Open-source code: https://github.com/OptimalScale/DetGPT
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Leaderboard for LLMs? [D]
Hi LMFlow Benchmark (https://github.com/OptimalScale/LMFlow) evaluates 31 open-source LLMs with an automatic metric: negative log likelihood.
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[R] LMFlow Benchmark: An Automatic Evaluation Framework for Open-Source LLMs
LMFlow: https://github.com/OptimalScale/LMFlow
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[R] Foundation Model Alignment with RAFT🛶 in LMFlow
Its implementation is available from https://github.com/OptimalScale/LMFlow.
- LMFlow – Toolkit for Finetuning and Inference of Large Foundation Models
chatgpt_macro_for_texstudio
What are some alternatives?
axolotl - Go ahead and axolotl questions
open-interpreter - A natural language interface for computers
CogVLM - a state-of-the-art-level open visual language model | 多模态预训练模型
EdgeGPT - Reverse engineered API of Microsoft's Bing Chat AI
llm-foundry - LLM training code for Databricks foundation models
gpt4free - The official gpt4free repository | various collection of powerful language models
const_layout - Official implementation of the MM'21 paper "Constrained Graphic Layout Generation via Latent Optimization" (LayoutGAN++, CLG-LO, and Layout evaluation)
gpt_academic - 为GPT/GLM等LLM大语言模型提供实用化交互接口,特别优化论文阅读/润色/写作体验,模块化设计,支持自定义快捷按钮&函数插件,支持Python和C++等项目剖析&自译解功能,PDF/LaTex论文翻译&总结功能,支持并行问询多种LLM模型,支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。
giskard - 🐢 Open-Source Evaluation & Testing framework for LLMs and ML models
chatgpt_academic - 为GPT/GLM提供图形交互界面,特别优化论文阅读润色体验,模块化设计支持自定义快捷按钮&函数插件,支持代码块表格显示,Tex公式双显示,新增Python和C++项目剖析&自译解功能,PDF/LaTex论文翻译&总结功能,支持并行问询多种LLM模型,支持清华chatglm等本地模型 [Moved to: https://github.com/binary-husky/gpt_academic]
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
gpt_llm - Multi-GPU setup for inference with GPT NeoX 20B and OPT-30B models in huggingface