llm-humaneval-benchmarks VS can-ai-code

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.

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.

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.

❗Note: In this study, we copy the scores for HumanEval and HumanEval+ from the LLM-Humaneval-Benchmarks. Notably, all the mentioned models generate code solutions for each problem utilizing a single attempt, and the resulting pass rate percentage is reported. Our WizardCoder generates answers using greedy decoding and tests with the same code.

I switched to RunPod from SageMaker in the middle of this process and boy am I happy I did. It is way cheaper and easier to scale for a project like this, and I highly recommend it. I have a set of tooling to run tests on it en masse now I am happy with - I will try to get my work up on the Github soon!: https://github.com/my-other-github-account/llm-humaneval-benchmarks

Also, my code I used for this eval is up at https://github.com/my-other-github-account/llm-humaneval-benchmarks/tree/8f3a77eb3508f33a88699aac1c4b10d5e3dc7de1

You can run a Q4 quant of a 70B model in about 40GB of RAM (+context). You're single user (batch size 1, bs=1) inference speed will be basically memory bottlenecked, so on a dual channel dedicated box you'd expect somewhere about 1 token/s. That's inference, prefill/prompt generation will take even longer (as your chat history grows) on CPU. So falls into the realm of technically possible, but not for real world use.

If you're looking specifically for CodeLlama 70B, Artificial Analysis https://artificialanalysis.ai/models/codellama-instruct-70b/... lists Perplexity, Together.ai, Deep Infra, and Fireworks as potential hosts, with Together.ai and Deepinfra at about $0.9/1M tokens, with about 30 tokens/s and about 300ms latency (time to first token).

For those looking for local coding models in specifically. I keep a list of LLM coding evals here: https://llm-tracker.info/evals/Code-Evaluation

On the EvalPlus Leaderboard, there about about 10 open models that rank higher than CodeLlama 70B, all smaller models: https://evalplus.github.io/leaderboard.html

A few other evals (worth cross-referencing to counter contamination, overfitting):

* CRUXEval Leaderboard https://crux-eval.github.io/leaderboard.html

* CanAiCode Leaderboard https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

* Big Code Models Leaderboard https://huggingface.co/spaces/bigcode/bigcode-models-leaderb...

From the various leaderboards, deepseek-ai/deepseek-coder-33b-instruct still looks like the best performing open model (it has a very liberal ethical license), followed by ise-uiuc/Magicoder-S-DS-6.7B (a deepseek-coder-6.7b-base fine tune). The former can be run as a Q4 quant on a single 24GB GPU (a used 3090 should run you about $700 atm), and the latter, if it works for you will run 4X faster and fit on even cheaper/weaker GPUs.

There's always recent developments, but two worth pointing out:

OpenCodeInterpreter - a new system that uses execution feedback and outperforms ChatGPT4 Code Interpreter that is fine-tuned off of the DeepSeek code models: https://opencodeinterpreter.github.io/

StarCoder2-15B just dropped and also looks competitive. Announcement and relevant links: https://huggingface.co/blog/starcoder2

This is a completely fair, but open question. Not to be a typical HN user, but when you say SOTA local, the question is really what benchmarks do you really care about in order to evaluate. Size, operability, complexity, explainability etc.

Working out what copilot models perform best has been a deep exercise for myself and has really made me evaluate my own coding style on what I find important and things I look out for when investigating models and evaluating interview candidates.

I think three benchmarks & leaderboards most go to are:

https://huggingface.co/spaces/bigcode/bigcode-models-leaderb... - which is the most understood, broad language capability leaderboad that relies on well understood evaluations and benchmarks.

https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul... - Also comprehensive, but primarily assesses Python and JavaScript.

https://evalplus.github.io/leaderboard.html - which I think is a better take on comparing models you intend to run locally as you can evaluate performance, operability and size in one visualisation.

Best of luck and I would love to know which models & benchmarks you choose and why.

Here is a leader board of some models

https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

Don't know how biased this leaderboard is, but I guess you could just give some of them a try and see for yourself.

- Can AI Code? - https://huggingface.co/spaces/mike-ravkine/can-ai-code-results

Check out https://github.com/the-crypt-keeper/can-ai-code for some ideas. I'd love to see more shootouts like this. Especially if they were spread out among a few different languages.

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...).

I have several sets of quant comparisons posted on my HF spaces, the caveat is my prompts are all "English to code": https://huggingface.co/spaces/mike-ravkine/can-ai-code-compa...

The dropdown at the top selects which comparison: Falcon compares GGML, Vicuna compares bits and bytes. I have some more comparisons planned, feel free to open an issue if you'd like to see something specific: https://github.com/the-crypt-keeper/can-ai-code

Yeah it seemed suspiciously high for HumanEval and it only ranks 14th for JS and 7th for Python on other benchmarks now: https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

WizardCoder is a bit of a problem since it's not llama 1/2 based but is its own 15B model and as such the support for it in anything practical is near nonexistent. WizardLM v1.2 looks like it may be worth checking out.

There are at least 4 different types of quants floating around HF (bitsandbytes, GGML, GPTQ and AWQ) so I dont know if a "GGML" column makes sense vs a more abstract way of linking quants to their base models. I am doing this and its fucking awful: https://github.com/the-crypt-keeper/can-ai-code/blob/main/models/models.yaml

Ah I meant this one but I see now it's WIP.