RasaGPT
sparsegpt
RasaGPT | sparsegpt | |
---|---|---|
8 | 16 | |
2,172 | 624 | |
- | 3.5% | |
5.6 | 2.4 | |
6 months ago | 28 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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RasaGPT
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(1/2) May 2023
RasaGPT: headless LLM chatbot platform built on top of Rasa and Langchain (https://github.com/paulpierre/RasaGPT)
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AI Weekly rundown (May 7- May 13, 2023): OpenAI's Shap·E, Multimodal GPT, Anthropic's constitutional AI and a lot more.
(Source)
- RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
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
What are some alternatives?
pandas-ai - Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
StableLM - StableLM: Stability AI Language Models
LLMChat - A Discord chatbot that supports popular LLMs for text generation and ultra-realistic voices for voice chat.
github-copilot-product-specific-terms
langchain-chatbot - AI Chatbot for analyzing/extracting information from data in conversational format.
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.
codeinterpreter-api - 👾 Open source implementation of the ChatGPT Code Interpreter
chat-ui - Open source codebase powering the HuggingChat app
langchain-llm-katas - This is a an open-source project designed to help you improve your skills with AI engineering using LLMs and the langchain library
intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
sketch - AI code-writing assistant that understands data content
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.