sparsegpt
promptfoo
sparsegpt | promptfoo | |
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16 | 20 | |
634 | 2,830 | |
5.0% | 21.2% | |
2.4 | 9.9 | |
about 1 month ago | 6 days ago | |
Python | TypeScript | |
Apache License 2.0 | MIT License |
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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
promptfoo
- Google CodeGemma: Open Code Models Based on Gemma [pdf]
- AI Infrastructure Landscape
- Promptfoo – Testing and Evaluation for LLMs
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Show HN: Prompt-Engineering Tool: AI-to-AI Testing for LLM
Super interesting. We've been experimenting with [promptfoo](https://github.com/promptfoo/promptfoo) at my work, and this looks very similar.
- GitHub – promptfoo/promptfoo: Test your prompts
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I asked 60 LLMs a set of 20 questions
In case anyone's interested in running their own benchmark across many LLMs, I've built a generic harness for this at https://github.com/promptfoo/promptfoo.
I encourage people considering LLM applications to test the models on their _own data and examples_ rather than extrapolating general benchmarks.
This library supports OpenAI, Anthropic, Google, Llama and Codellama, any model on Replicate, and any model on Ollama, etc. out of the box. As an example, I wrote up an example benchmark comparing GPT model censorship with Llama models here: https://promptfoo.dev/docs/guides/llama2-uncensored-benchmar.... Hope this helps someone.
- Ask HN: Prompt Manager for Developers
- DeepEval – Unit Testing for LLMs
- Show HN: Knit – A Better LLM Playground
- Show HN: CLI for testing and evaluating LLM outputs
What are some alternatives?
StableLM - StableLM: Stability AI Language Models
shap-e - Generate 3D objects conditioned on text or images
github-copilot-product-specific-terms
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
chat-ui - Open source codebase powering the HuggingChat app
WizardLM - Family of instruction-following LLMs powered by Evol-Instruct: WizardLM, WizardCoder and WizardMath
intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
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.
litellm - Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
coriander - Build NVIDIA® CUDA™ code for OpenCL™ 1.2 devices
ChainForge - An open-source visual programming environment for battle-testing prompts to LLMs.