axolotl
LMFlow
axolotl | LMFlow | |
---|---|---|
29 | 10 | |
5,811 | 8,023 | |
9.3% | 3.3% | |
9.8 | 9.6 | |
5 days ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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axolotl
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Ask HN: Most efficient way to fine-tune an LLM in 2024?
The approach I see used is axolotl with QLoRA using cloud GPUs which can be quite cheap.
https://github.com/OpenAccess-AI-Collective/axolotl
- FLaNK AI - 01 April 2024
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LoRA from Scratch implementation for LLM finetuning
https://github.com/OpenAccess-AI-Collective/axolotl
- Optimized Triton Kernels for full fine tunes
- Axolotl
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Let’s Collaborate to Build a High-Quality, Open-Source Dataset for LLMs!
One option is to look at what Axolotl uses. They have a list of different dataset formats that they support. They're mostly in JSON with specific field names, so you could start putting a dataset together with a text editor or a JSON editor.
- Axolotl: Streamline fine-tuning of AI models
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Dataset Creation Tools?
You can save that overall set into a json file and load it up as training data in whatever you're using. I'm using axolotl for it at the moment. Though a GUI based option is probably best for the first couple of tries until you get a feel for the options.
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Progress on Reproducing Phi-1/1.5
Looking forward to the results! If it turns out the dataset is reproducible, then it might be a good candidate for ReLora training on axolotl!
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
What are some alternatives?
signal-cli - signal-cli provides an unofficial commandline, JSON-RPC and dbus interface for the Signal messenger.
CogVLM - a state-of-the-art-level open visual language model | 多模态预训练模型
gpt-llm-trainer
chatgpt_macro_for_texstudio - The ChatGPT Macro for TeXstudio is a user-friendly integration that connects TeXstudio with OpenAI's API.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
llm-foundry - LLM training code for Databricks foundation models
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
const_layout - Official implementation of the MM'21 paper "Constrained Graphic Layout Generation via Latent Optimization" (LayoutGAN++, CLG-LO, and Layout evaluation)
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
giskard - 🐢 Open-Source Evaluation & Testing framework for LLMs and ML models
OpenPipe - Turn expensive prompts into cheap fine-tuned models
qlora - QLoRA: Efficient Finetuning of Quantized LLMs