--- title: "code-eval" type: "tool" slug: "abacaj-code-eval" canonical_url: "https://www.graphcanon.com/tools/abacaj-code-eval" github_url: "https://github.com/abacaj/code-eval" homepage_url: null stars: 429 forks: 37 primary_language: "Python" license: "MIT" archived: false categories: ["evaluation-observability", "llm-frameworks"] tags: ["humaneval", "python", "wizardcoder"] updated_at: "2026-07-11T23:43:44.167333+00:00" --- # code-eval > Run evaluation on LLMs using human-eval benchmark Run evaluation on LLMs using human-eval benchmark ## Facts - Repository: https://github.com/abacaj/code-eval - Stars: 429 · Forks: 37 · Open issues: 5 · Watchers: 9 - Primary language: Python - License: MIT - Last pushed: 2023-09-12T03:03:56+00:00 ## Trust & health _Signals computed from public GitHub metadata. Not a security guarantee._ - Maintenance: Dormant (computed 2026-07-11T23:43:41.158Z) - Security scan: Findings present (0 critical, 0 high, 0 medium, 73 low) · last scan 2026-07-11T23:43:41.595Z - Full report: [trust report](/tools/abacaj-code-eval/trust.md) · [JSON](https://www.graphcanon.com/api/graphcanon/tools/abacaj-code-eval/trust) ## Categories - [Evaluation & Observability](/categories/evaluation-observability.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags humaneval, python, wizardcoder ## Category neighbours (exploratory) _Same-category tools for discovery only - not curated alternatives. Cap shown at six._ - [awesome](/tools/sindresorhus-awesome.md) - 😎 Curated list of awesome topics including hardware resources (★ 484,026) [Active] - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT is the vision of accessible AI for everyone, to use and to build on. 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Some scripts were adjusted from wizardcoder repo ([process_eval.py](https://github.com/nlpxucan/WizardLM/blob/main/WizardCoder/src/process_humaneval.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](https://huggingface.co/sahil2801/replit-code-instruct-glaive) | 3B | 63.5% | 67% | | | [WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) | 15B | 57% | 68.9% | | | [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) | 15B | 34.6% | 48.7% | | | [openchat/opencoderplus](https://huggingface.co/openchat/opencoderplus) | 15B | 27.3% | 43.9% | | | [teknium/Replit-v1-CodeInstruct-3B](https://huggingface.co/teknium/Replit-v1-CodeInstruct-3B) | 3B | 25.8% | 42.6% | | | [teknium/Replit-v2-CodeInstruct-3B](https://huggingface.co/teknium/Replit-v2-CodeInstruct-3B) | 3B | 21.5% | 31% | | | [replit-code-v1-3b](https://huggingface.co/replit/replit-code-v1-3b) | 3B | 17.1% | 29.8% | | | [mpt-7b](https://huggingface.co/mosaicml/mpt-7b) | 7B | 15.9% | 23.7% | | | [xgen-7b-8k-base](https://huggingface.co/Salesforce/xgen-7b-8k-base) | 7B | 14.9% | 22.5% | | | [openllama-7b-v2](https://huggingface.co/openlm-research/open_llama_7b) | 7B | 14% | 23.1% | | | [llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) | 7B | 13.1% | 21.9% | | | [llama-7b](https://huggingface.co/huggyllama/llama-7b) | 7B | 12.1% | 18.9% | | | [mpt-30b](https://huggingface.co/mosaicml/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 ```sh python -m venv env && source env/bin/activate ``` Install dependencies ```sh pip install -r requirements.txt ```` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/abacaj-code-eval`](/api/graphcanon/tools/abacaj-code-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_