LMFlow
transformers
LMFlow | transformers | |
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10 | 178 | |
8,042 | 125,741 | |
3.5% | 2.0% | |
9.6 | 10.0 | |
5 days ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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
transformers
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XLSTM: Extended Long Short-Term Memory
Fascinating work, very promising.
Can you summarise how the model in your paper differs from this one ?
https://github.com/huggingface/transformers/issues/27011
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AI enthusiasm #9 - A multilingual chatbot📣🈸
transformers is a package by Hugging Face, that helps you interact with models on HF Hub (GitHub)
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Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
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Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
The HuggingFace transformers library already has support for a similar method called prompt lookup decoding that uses the existing context to generate an ngram model: https://github.com/huggingface/transformers/issues/27722
I don't think it would be that hard to switch it out for a pretrained ngram model.
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AI enthusiasm #6 - Finetune any LLM you want💡
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
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Schedule-Free Learning – A New Way to Train
* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013
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Gemma doesn't suck anymore – 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
- HuggingFace Transformers: Qwen2
- HuggingFace Transformers Release v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2
- HuggingFace: Support for the Mixtral Moe
What are some alternatives?
axolotl - Go ahead and axolotl questions
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
CogVLM - a state-of-the-art-level open visual language model | 多模态预训练模型
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
chatgpt_macro_for_texstudio - The ChatGPT Macro for TeXstudio is a user-friendly integration that connects TeXstudio with OpenAI's API.
llama - Inference code for Llama models
llm-foundry - LLM training code for Databricks foundation models
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
const_layout - Official implementation of the MM'21 paper "Constrained Graphic Layout Generation via Latent Optimization" (LayoutGAN++, CLG-LO, and Layout evaluation)
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
giskard - 🐢 Open-Source Evaluation & Testing framework for LLMs and ML models
huggingface_hub - The official Python client for the Huggingface Hub.