supercharger
JARVIS
supercharger | JARVIS | |
---|---|---|
13 | 52 | |
346 | 23,136 | |
- | 1.0% | |
6.6 | 7.2 | |
about 1 year ago | 28 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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supercharger
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Claude 2
Since I've been on a AI code-helper kick recently. According to the post, Claude 2 now 71.2%, a significant upgrade from 1.3 (56.0%). It isn't specified whether this is pass@1 or pass@10.
For comparison:
* GPT-4 claims 85.4 on HumanEval, in a recent paper https://arxiv.org/pdf/2303.11366.pdf GPT-4 was tested at 80.1 pass@1 and 91 pass@1 using their Reflexion technique. They also include MBPP and Leetcode Hard benchmark comparisons
* WizardCoder, a StarCoder fine-tune is one of the top open models, scoring a 57.3 pass@1, model card here: https://huggingface.co/WizardLM/WizardCoder-15B-V1.0
* The best open model I know of atm is replit-code-instruct-glaive, a replit-code-3b fine tune, which scores a 63.5% pass@1. An independent developer abacaj has reproduced that announcement as part of code-eval, a repo for getting human-eval results: https://github.com/abacaj/code-eval
Those interested in this area may also want to take a look at this repo https://github.com/my-other-github-account/llm-humaneval-ben... that also ranks with Eval+, the CanAiCode Leaderboard https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul... and airate https://github.com/catid/supercharger/tree/main/airate
Also, as with all LLM evals, to be taken with a grain of salt...
Liu, Jiawei, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. “Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.” arXiv, June 12, 2023. https://doi.org/10.48550/arXiv.2305.01210.
- Let's be honest: none of the models can code well
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April 2023
Leverage locally-hosted Large Language Models to write software + unit tests (https://github.com/catid/supercharger)
- What coding llm is the best?
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Is there such a thing as local Llamas integrated into VSCode?
supercharger Write Software + unit tests for you, based on Baize-30B 8bit, using model parallelism
- I have a project in my own programming language, abusing both lexical and syntactic macros. I want to do a refactoring tasks on it. I don't have a GPU, but 14-core CPU. Should I pay for cloud or there are local ways to do such task on my laptop? Which model is better for programming?
- What is the best open source model/program to help index and debug code?
- Leverage locally-hosted Large Language Models to write software and unit tests
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Can LLMs do static code analysis?
Added support for 65B LLaMa model to https://github.com/catid/supercharger tonight. It runs faster than Baize 30B (maybe due to lack of adapter) and only slightly slower than Galpaca 30B. Benchmarks here: https://docs.google.com/spreadsheets/d/1TYBNr_UPJ7wCzJThuk5ysje7K1x-_62JhBeXDbmrjA8/edit?usp=sharing
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Benchmarks for LLMs on Consumer Hardware
Here's the code that loads it: https://github.com/catid/supercharger/blob/main/server/model_koala.py
JARVIS
- FLaNK Stack 26 February 2024
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Overview: AI Assembly Architectures
Jarvis: github.com/microsoft/JARVIS
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When will we get JARVIS?
You can build it yourself now. https://github.com/microsoft/JARVIS
- How to build the Geth (networked intelligence, decentralized AGI)
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Off-topic: What NVIDIA GPU do I need to run privateGPT or Alpaca-Lora for code translations, debugging, unit tests, etc?
https://github.com/microsoft/JARVIS (when ready says >=24GB VRAM)
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Apple announces Apple Silicon Mac Pro powered by M2 Ultra
Can be. There are projects that run fully locally like Microsoft’s Jarvis. https://github.com/microsoft/JARVIS
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April 2023
JARVIS, a system to connect LLMs with ML community (https://github.com/microsoft/JARVIS)
- Nvidia's GH200 AI supercomputers could build 'giant' AI models more powerful than GPT-4
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A Lightweight HuggingGPT Implementation w/ Langchain + Thoughts on Why JARVIS Fails to Deliver
HuggingGPT is a clever idea to boost the capabilities of LLM Agents, and enable them to solve “complicated AI tasks with different domains and modalities”. In short, it uses ChatGPT to plan tasks, select models from Hugging Face (HF), format inputs, execute each subtask via the HF Inference API, and summarise the results. JARVIS tries to generalise this idea, and create a framework to “connect LLMs with the ML community”, which Microsoft Research claims “paves a new way towards advanced artificial intelligence”.
- Edit videos through intuitive ChatGPT conversations
What are some alternatives?
developer - the first library to let you embed a developer agent in your own app!
AutoGPT - AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
gptest - GPTest VS Code Extension
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
walter - AI-powered software development assistant built right into GitHub so it can act as your junior developer.
babyagi
llm-humaneval-benchmarks
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/AutoGPT]
evaporate - This repo contains data and code for the paper "Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes"
visual-chatgpt - Official repo for the paper: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models [Moved to: https://github.com/microsoft/TaskMatrix]
locai - Connect to Kobold API through VS Code
dalai - The simplest way to run LLaMA on your local machine