simple-llm-finetuner
GPTQ-for-LLaMa
simple-llm-finetuner | GPTQ-for-LLaMa | |
---|---|---|
12 | 75 | |
1,977 | 2,924 | |
- | - | |
10.0 | 8.6 | |
5 months ago | 9 months ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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simple-llm-finetuner
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Ask HN: Resource to learn how to train and use ML Models
Just the appropriate reddit groups and follow folks on twitter, plus use a search engine.
1. Learn to run a model, checkout llama.cpp Tons of free models on huggingface.com
2. Learn to finetune a model - https://github.com/lxe/simple-llm-finetuner
3. Learn to train one. PyTorch, TensorFlow, HuggingFace libraries, etc.
Good luck.
- How can I train my custom dataset on top of Vicuna?
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[D] The best way to train an LLM on company data
So as far as set up goes, you just need to: “”” Git clone https://github.com/lxe/simple-llama-finetuner Cd simple-llama-finetuner Pip install -r requirements.txt Python app.py ## if you’re on a remote machine (Paperspace is my go to) then you may need to edit the last line of this script to set ‘share=True’ in the launch args “””
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Show HN: Document Q&A with GPT: web, .pdf, .docx, etc.
oobabooga's textgen webui has a tab for fine tuning now. You only need a single consumer GPU to fine tune up to 33B parameter models at a rate of about 200 epochs per hour, per GPU.
There are also one-click finetuning projects which run on free Google Colab GPUs like https://github.com/lxe/simple-llama-finetuner
It's easy and not complex at all.
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How do I fine tune 4 bit or 8 bit models?
for a single 4090, easiest way to get started and simple to use: https://github.com/lxe/simple-llama-finetuner
- Are there publicly available datasets other than Alpaca that we can use to fine-tune LLaMA?
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
- [Project] Finetune LLaMA-7B on commodity GPUs (and Colab) using your own text
GPTQ-for-LLaMa
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[P] Early in 2023 I put in a lot of work on a new machine learning project. Now I'm not sure what to do with it.
First I want to make it clear this is not a self promotion post. I hope many machine learning people come at me with questions or comments about this project. A little background about myself. I did work on the 4 bits quantization of LLaMA using GPTQ. (https://github.com/qwopqwop200/GPTQ-for-LLaMa). I've been studying AI in-depth for many years now.
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GPT-4 Details Leaked
Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .
If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...
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Rambling
I use gptq-for-llama - from this https://github.com/qwopqwop200/GPTQ-for-LLaMa and Pygmalion 7B.
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Now that ExLlama is out with reduced VRAM usage, are there any GPTQ models bigger than 7b which can fit onto an 8GB card?
exllama is an optimized implementation of GPTQ-for-LLaMa, allowing you to run 4-bit quantized language models with GPU at great speeds.
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GGML – AI at the Edge
With a single NVIDIA 3090 and the fastest inference branch of GPTQ-for-LLAMA https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/fastest-i..., I get a healthy 10-15 tokens per second on the 30B models. IMO GGML is great (And I totally use it) but it's still not as fast as running the models on GPU for now.
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New quantization method AWQ outperforms GPTQ in 4-bit and 3-bit with 1.45x speedup and works with multimodal LLMs
And exactly what Triton version are they comparing against? I just tried the latest version of this, and on my 4090/12900K I get 77 tokens per second for Llama 7B-128g. My own GPTQ CUDA implementation gets 151 tokens/second on the same model, same hardware. That makes it 96% faster, whereas AWQ is only 79% faster. For 30B-128g I'm currently only getting a 110% speedup over Triton compared to their 178%, but it still seems a little disingenuous to compare against their own CUDA implementation only, when they're trying to present the quantization method as being faster for inference.
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Introducing Basaran: self-hosted open-source alternative to the OpenAI text completion API
Thanks for the explanation. I think some repos, like text generation webui used gptq for llama (I don't know if it's this repo or another one), anyway most repo that I saw use external things (like gptq for llama)
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How to use AMD GPU?
cd ../.. git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install -r requirements.txt mkdir -p ../text-generation-webui/repositories ln -s ../../GPTQ-for-LLaMa ../text-generation-webui/repositories/GPTQ-for-LLaMa
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Help needed with installing quant_cuda for the WebUI
cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa pip install -r requirements.txt
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The installed version of bitsandbytes was compiled without GPU support
# To use the GPTQ models I need to Install GPTQ-for-LLaMa and the monkey patch mkdir repositories cd repositories git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton cd GPTQ-for-LLaMa pip install ninja pip install -r requirements.txt cd cd text-generation-webui # download random model python download-model.py xxx/yyy # try to start the gui python server.py # It returns this warning but it runs bin /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable. warn("The installed version of bitsandbytes was compiled without GPU support. " /home/gm/miniconda3/envs/chat/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
llama.cpp - LLM inference in C/C++
paper-qa - LLM Chain for answering questions from documents with citations
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
minimal-llama
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
OpenChatKit
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI