code-eval
open_llama
code-eval | open_llama | |
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5 | 52 | |
349 | 7,211 | |
- | 0.9% | |
8.0 | 5.3 | |
8 months ago | 10 months ago | |
Python | ||
MIT License | Apache License 2.0 |
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code-eval
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Ask HN: LLM Leaderboard for Code Generation?
You're looking for "HumanEval" tests. Not saying this is the best way to test it, but it's the only standard test I know of that code models are compared with and are commonly benchmarked for
The current best models you'd want to try that I'm aware of is WizardCoder(15B), Starcoder(15B), and replit's code model(3B). Replit's instruct model is interesting because of it's competitive performance while only being a 3B model so it's the easiest/fastest to use.
https://github.com/abacaj/code-eval - This is a large mostly up to date list of benchmarks
https://huggingface.co/WizardLM/WizardCoder-15B-V1.0 - has a chart with a mostly up to date comparison
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LLaMA2 Chat 70B outperformed ChatGPT
You will want to look at HumanEval (https://github.com/abacaj/code-eval) and Eval+ (https://github.com/my-other-github-account/llm-humaneval-ben...) results for coding.
While Llama2 is an improvement over LLaMA v1, it's still nowhere near even the best open models (currently, sans test contamination, WizardCoder-15B, a StarCoder fine tune is at top). It's really not a competition atm though, ChatGPT-4 wipes the floor for coding atm.
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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. βIs Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.β arXiv, June 12, 2023. https://doi.org/10.48550/arXiv.2305.01210.
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Which LLM works for taboo questions or programming like webscraping?
To get an idea of programming performance, my can-ai-code Leaderboard is freshly updated this morning, but also check out the excellent llm-eval and code-eval leaderboards.
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GPT-4 API general availability
In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs.
You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I think home-rolled evals like llm-jeopardy [2] and local-llm-comparison [3] by hobbyists are more useful than most of the benchmark rankings.
That being said, personally I mostly use GPT-4 for code assistance to that's what I'm most interested in, and the latest code assistants are scoring quite well: https://github.com/abacaj/code-eval - a recent replit-3b fine tune the human-eval results for open models (as a point of reference, GPT-3.5 gets 60.4 on pass@1 and 68.9 on pass@10 [4]) - I've only just started playing around with it since replit model tooling is not as good as llamas (doc here: https://llm-tracker.info/books/howto-guides/page/replit-mode...).
I'm interested in potentially applying reflexion or some of the other techniques that have been tried to even further increase coding abilities. (InterCode in particular has caught my eye https://intercode-benchmark.github.io/)
[1] https://github.com/turboderp/exllama#results-so-far
[2] https://github.com/aigoopy/llm-jeopardy
[3] https://github.com/Troyanovsky/Local-LLM-comparison/tree/mai...
[4] https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder
open_llama
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How Open is Generative AI? Part 2
The RedPajama dataset was adapted by the OpenLLaMA project at UC Berkeley, creating an open-source LLaMA equivalent without Metaβs restrictions. The model's later version also included data from Falcon and StarCoder. This highlights the importance of open-source models and datasets, enabling free repurposing and innovation.
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GPT-4 API general availability
OpenLLaMA is though. https://github.com/openlm-research/open_llama
All of these are surmountable problems.
We can beat OpenAI.
We can drain their moat.
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Recommend me a computer for local a.i for 500 $
#1: π Open-source Reproduction of Meta AIβs LLaMA OpenLLaMA-13B released. (trained for 1T tokens) | 0 comments #2: π #1 on HuggingFace.co's Leaderboard Model Falcon 40B is now Free (Apache 2.0 License) | 0 comments #3: π Have you seen this repo? "running LLMs on consumer-grade hardware. compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!" | 0 comments
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Who is openllama from?
Trained OpenLLaMA models are from the OpenLM Research team in collaboration with Stability AI: https://github.com/openlm-research/open_llama
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Personal GPT: A tiny AI Chatbot that runs fully offline on your iPhone
I can't use Llama or any model from the Llama family, due to license restrictions. Although now there's also the OpenLlama family of models, which have the same architecture but were trained on an open dataset (RedPajama, the same dataset the base model in my app was trained on). I'd love to pursue the direction of extended context lengths for on-device LLMs. Likely in a month or so, when I've implemented all the product feature that I currently have on my backlog.
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XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens
https://github.com/openlm-research/open_llama#update-0615202...).
XGen-7B is probably the superior 7B model, it's trained on more tokens and a longer default sequence length (although both presumably can adopt SuperHOT (Position Interpolation) to extend context), but larger models still probably perform better on an absolute basis.
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MosaicML Agrees to Join Databricks to Power Generative AI for All
Compare it to openllama. It github doesn't have a single script on how to do anything.
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Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML
OpenLLaMA models up to 13B parameters have now been trained on 1T tokens:
https://github.com/openlm-research/open_llama
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Containerized AI before Apocalypse π³π€
The deployed LLM binary, orca mini, has 3 billion parameters. Orca mini is based on the OpenLLaMA project.
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AI β weekly megathread!
OpenLM Research released its 1T token version of OpenLLaMA 13B - the permissively licensed open source reproduction of Meta AI's LLaMA large language model. [Details].
What are some alternatives?
llm-humaneval-benchmarks
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
llama.cpp - LLM inference in C/C++
azure-search-openai-demo - A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
visqol - Perceptual Quality Estimator for speech and audio
gpt4all - gpt4all: run open-source LLMs anywhere
llm-humaneval-ben
gorilla - Gorilla: An API store for LLMs
ggml - Tensor library for machine learning