code-eval

What

This is a repo I use to run human-eval on code models, adjust as needed. Some scripts were adjusted from wizardcoder repo (process_eval.py). The evaluation code is duplicated in several files, mostly to handle edge cases around model tokenizing and loading (will clean it up).

Results

Table is sorted by pass@1 score.

model	size	pass@1	pass@10	screenshot
sahil2801/replit-code-instruct-glaive	3B	63.5%	67%
WizardCoder-15B-V1.0	15B	57%	68.9%
bigcode/starcoder	15B	34.6%	48.7%
openchat/opencoderplus	15B	27.3%	43.9%
teknium/Replit-v1-CodeInstruct-3B	3B	25.8%	42.6%
teknium/Replit-v2-CodeInstruct-3B	3B	21.5%	31%
replit-code-v1-3b	3B	17.1%	29.8%
mpt-7b	7B	15.9%	23.7%
xgen-7b-8k-base	7B	14.9%	22.5%
openllama-7b-v2	7B	14%	23.1%
llama-2-7b	7B	13.1%	21.9%
llama-7b	7B	12.1%	18.9%
mpt-30b	30B	pending	pending	pending

FAQ

Why is there a discrepancy on some of the scores between official numbers?

Because it is not obvious or published what prompt or processing the official models used to conduct their evaluation on this benchmark. The goal here is to try and best reproduce those numbers, in many cases it is possible to get very close to the published numbers.

All of the scores here were run independently of any published numbers and are reproducible by cloning the repo and following the setup.

Why do some models have a filter_code post generation step?

Base models can in many cases repeat outputs, breaking the benchmark scores. Instruct models don't have this problem and so you won't see this step, they tend to output a end of sequence token.

Setup

Create python environment

python -m venv env && source env/bin/activate

Install dependencies

pip install -r requirements.txt