sparsegpt
serge
sparsegpt | serge | |
---|---|---|
16 | 40 | |
634 | 5,553 | |
5.0% | 0.9% | |
2.4 | 9.8 | |
about 1 month ago | 8 days ago | |
Python | Svelte | |
Apache License 2.0 | Apache License 2.0 |
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sparsegpt
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(1/2) May 2023
SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot (https://arxiv.org/abs/2301.00774)
- Why Falcon going Apache 2.0 is a BIG deal for all of us.
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New Open-source LLMs! 🤯 The Falcon has landed! 7B and 40B
There is this : https://github.com/IST-DASLab/sparsegpt
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Webinar: Running LLMs performantly on CPUs Utilizing Pruning and Quantization
Check the paper here, it's intersting: https://arxiv.org/abs/2301.00774
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OpenAI chief goes before US Congress to propose licenses for building AI
There's no chance that we've peeked from a bang for buck sense - we still haven't adequately investigated sparse networks.
Relevantish: https://arxiv.org/abs/2301.00774
The fact that we can reach those levels of sparseness with pruning also indicates that we're not doing a very good job of generating the initial network conditions.
Being able to come up with trainable initial settings for sparse networks across different topologies is hard, but given that we've had a degree of success with pre-trained networks, pre-training and pre-pruning might also allow for sparse networks with minimally compromised learning capabilities.
If it's possible to pre-train composable network modules, it might also be feasible to define trainable sparse networks with significantly relaxed topological constraints.
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How to run Llama 13B with a 6GB graphics card
Training uses gradient descent, so you want to have good precision during that process. But once you have the overall structure of the network, https://arxiv.org/abs/2210.17323 (GPTQ) showed that you can cut down the precision quite a bit without losing a lot of accuracy. It seems you can cut down further for larger models. For the 13B Llama-based ones, going below 5 bit per parameter is noticeably worse, but for 30B models you can do 4 bits.
The same group did another paper https://arxiv.org/abs/2301.00774 which shows that in addition to reducing the precision of each parameter, you can also prune out a bunch of parameters entirely. It's harder to apply this optimization because models are usually loaded into RAM densely, but I hope someone figures out how to do it for popular models.
- SparseGPT: Language Models Can Be Accurately Pruned in One-Shot
serge
- Show HN: I made an app to use local AI as daily driver
- chatgpt alternative
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Show HN: LlamaGPT – Self-hosted, offline, private AI chatbot, powered by Llama 2
Very cool, this looks like a combination of chatbot-ui and llama-cpp-python? A similar project I've been using is https://github.com/serge-chat/serge. Nous-Hermes-Llama2-13b is my daily driver and scores high on coding evaluations (https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...).
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LeCun: Qualcomm working with Meta to run Llama-2 on mobile devices
You might be pleased to hear that nothing really stops you from doing this today. If you ran Serge[0] on a Mac with Tailscale, you could hack together a decently-accelerated Llama chatbot.
[0] https://github.com/serge-chat/serge
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Chatbot frontend library in Svelte?
Cannot help you with libraries specifically but both Serge and ChatUI are built using SvelteKit, so the code might be of some use to you.
- We’re back and…
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Best way to use AMD CPU and GPU
Serge made it really easy for me to get started, but it all CPU-based.
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Need Help
All that said this project probably solves your problem: https://github.com/serge-chat/serge
- Are you selfhosting a ChatGPT alternative?
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What the hell??
You can play a little bit with more straightforward local models (the simplest to setup is https://github.com/nsarrazin/serge ), to see that any LLM is basically a party trick.
What are some alternatives?
StableLM - StableLM: Stability AI Language Models
gpt4all - gpt4all: run open-source LLMs anywhere
github-copilot-product-specific-terms
langflow - ⛓️ Langflow is a dynamic graph where each node is an executable unit. Its modular and interactive design fosters rapid experimentation and prototyping, pushing hard on the limits of creativity.
promptfoo - Test your prompts, models, and RAGs. Catch regressions and improve prompt quality. LLM evals for OpenAI, Azure, Anthropic, Gemini, Mistral, Llama, Bedrock, Ollama, and other local & private models with CI/CD integration.
llama.cpp - LLM inference in C/C++
chat-ui - Open source codebase powering the HuggingChat app
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
geov - The GeoV model is a large langauge model designed by Georges Harik and uses Rotary Positional Embeddings with Relative distances (RoPER). We have shared a pre-trained 9B parameter model.
llama-gpt - A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device. New: Code Llama support!