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DeepSeek Coder is a series of pre-trained code language models (1B–33B parameters) trained from scratch on 2T tokens (87% code, 13% natural language) that achieve state-of-the-art performance on code generation benchmarks like HumanEval, MultiPL-E, and DS-1000. The models support project-level code completion and infilling via a 16K context window and fill-in-the-blank task, spanning 80+ programming languages including Python, Java, JavaScript, Go, C++, and Rust. Monorepo structure: /Evaluation/HumanEval/ contains multi-language benchmark data (JSONL for Python, Java, C++, Go, JavaScript, Rust, etc.) and evaluation harness (human_eval/evaluate_functional_correctness.py, human_eval/execution.py); /Evaluation/DS-1000/ and /Evaluation/LeetCode/ hold additional benchmark datasets; core model inference code lives in HuggingFace model repos (not in this tree). Evaluation utilities in utils/ (dataset.py, utils.py) handle test case loading and execution.

ML engineers and researchers building or evaluating code generation systems; developers integrating advanced code completion into IDEs or development tools; teams benchmarking LLMs on coding tasks across multiple languages. Specifically useful for those needing to understand or reproduce state-of-the-art open-source code model performance.

This is an actively maintained research project with substantial artifacts (model weights on HuggingFace, extensive multi-language evaluation datasets, formal paper on arXiv 2401.14196). The repository contains production-grade evaluation infrastructure (HumanEval, DS-1000, LeetCode benchmarks) and is backed by DeepSeek (a credible research organization). However, as a model-release repo rather than a deployed service, maturity focuses on reproducibility and benchmarking rigor rather than long-term API stability.

Dependencies are relatively lightweight and well-maintained (torch 2.0.1, transformers 4.35.0, deepspeed 0.12.2), all stable releases. The codebase is primarily evaluation/documentation with minimal custom infrastructure, reducing maintenance burden. Main risks: heavy reliance on external HuggingFace model downloads (network/availability dependency) and the evaluation harness assumes specific language runtime setups (Java jar, multiple language toolchains) which can be fragile across environments. No visible CI/CD pipeline in the file list suggests manual testing.

The repository is static post-release—no active development PRs or commits visible in file structure. It primarily serves as a distribution point for benchmark datasets, evaluation code, and reproducibility artifacts linked to the published paper (2401.14196). The focus is on maintaining evaluation correctness across 14+ language implementations in HumanEval rather than rolling new features.

git clone https://github.com/deepseek-ai/DeepSeek-Coder.git
cd DeepSeek-Coder
pip install torch==2.0.1 transformers==4.35.0 deepseek-ai/DeepSeek-Coder transformers accelerate==0.24.1 datasets
cd Evaluation/HumanEval
python humaneval.py --model_path <hf_model_id> --output_path results.jsonl

Daily commands: Evaluation entrypoint: cd Evaluation/HumanEval && python humaneval.py --model_path deepseek-ai/deepseek-coder-1b-base --output_path results.jsonl (see eval.sh for full example). For instruction-tuned models: python eval_instruct.py --model_path deepseek-ai/deepseek-coder-1b-instruct. No development server—this is a batch evaluation framework, not a service.

• Evaluation/HumanEval/humaneval.py — Main entry point for HumanEval benchmark evaluation; orchestrates code generation and functional correctness testing across multiple programming languages.
• Evaluation/HumanEval/human_eval/evaluate_functional_correctness.py — Core evaluation logic that executes generated code and validates correctness; used by all benchmark suites (HumanEval, MBPP, LeetCode).
• Evaluation/HumanEval/human_eval/execution.py — Handles safe code execution and timeout management; critical for preventing evaluation hangs and security issues during test runs.
• Evaluation/MBPP/mbpp.py — MBPP benchmark evaluation harness; demonstrates evaluation patterns reused across multiple benchmark datasets.
• Evaluation/LeetCode/evaluate_leetcode.py — LeetCode benchmark evaluation with vLLM integration; shows how to scale inference for large evaluation runs.
• Evaluation/HumanEval/utils/dataset.py — Dataset loading and preparation utilities shared across all benchmarks; handles prompt formatting and multi-language support.
• Evaluation/PAL-Math/README.md — Documents program-aided language (PAL) approach for math reasoning; explains how to extend evaluation to non-functional-correctness domains.

Add a New Programming Language to HumanEval

1. Create JSONL dataset file with language-specific test cases and problem translations (Evaluation/HumanEval/data/humaneval-{lang}.jsonl)
2. Add language extension mapping and execution template in dataset loader (Evaluation/HumanEval/utils/dataset.py)
3. Update test_config.yaml with language-specific compiler/runtime configuration (Evaluation/HumanEval/test_config.yaml)
4. Run humaneval.py with --language flag to evaluate on new language (Evaluation/HumanEval/humaneval.py)

Add a New Code Benchmark Dataset

1. Create new evaluation directory and add README documenting benchmark details and metrics (Evaluation/{NewBenchmark}/README.md)
2. Create dataset JSONL files with problem definitions, test cases, and expected outputs (Evaluation/{NewBenchmark}/data/{benchmark_name}.jsonl)
3. Copy and adapt human_eval module to handle benchmark-specific evaluation logic (Evaluation/{NewBenchmark}/human_eval/evaluate_functional_correctness.py)
4. Create main benchmark runner script with model loading and result reporting (Evaluation/{NewBenchmark}/{benchmark_name}.py)

Integrate New Model for Evaluation

1. Update test_config.yaml with new model identifier, path, and generation parameters (Evaluation/HumanEval/test_config.yaml)
2. Modify evaluation harness to load model from config and generate completions (Evaluation/HumanEval/humaneval.py)
3. For large-scale evals, add model to vLLM inference wrapper for batched generation (Evaluation/LeetCode/vllm_inference.py)
4. Run evaluation script and collect pass@k metrics for benchmarking (Evaluation/HumanEval/eval.sh)

Extend Evaluation to Non-Functional-Correctness Tasks

1. Create new benchmark directory with task-specific README explaining evaluation criteria (Evaluation/{NewTask}/README.md)
2. Implement custom evaluation logic for metrics (e.g., BLEU, semantic similarity, math correctness) (Evaluation/{NewTask}/human_eval/evaluation.py)
3. See PAL-Math evaluation as reference for reasoning tasks using program execution (Evaluation/PAL-Math/README.md)
4. Create runner script that loads data, generates responses, and computes custom metrics (Evaluation/{NewTask}/{task_name}.py)

• Transformers (Hugging Face) — Standard library for loading and running pre-trained DeepSeek Coder models (1B-33B); enables easy model swapping for benchmarking.
• PyTorch 2.0.1 — Underlying deep learning framework for model inference with CUDA support; enables GPU acceleration for fast code generation.
• JSONL datasets — Lightweight streaming format for large multi-language benchmark datasets; supports line-by-line loading without full in-memory parsing.
• vLLM — Efficient batched inference engine used for LeetCode and large-scale evaluation; dramatically speeds up generation throughput (10-50x faster than naive inference).
• Accelerate — Simplifies distributed inference and multi-GPU evaluation; handles device placement and sharding for scaling to multiple GPUs/TPUs.

• Separate evaluation modules per benchmark (HumanEval, MBPP, LeetCode) instead of unified framework

  • Why: Each benchmark has different test case formats, metrics, and language requirements; duplication is acceptable for clarity and benchmark-specific optimizations.
  • Consequence: Code duplication across benchmarks (e.g., execution.py); harder to maintain consistency but easier to customize per benchmark.

• Synchronous code execution in sandbox with timeouts instead of async/streaming

  • Why: Ensures deterministic test execution, prevents resource leaks, and simplifies pass/fail logic; required for reproducible metrics.
  • Consequence: Evaluation is I/O-bound and slower for many problems; batch evaluation with vLLM mitigates this but adds complexity.

• Support 25+ programming languages in HumanEval rather than Python-only

  • Why: Demonstrates cross-language generalization of DeepSeek Coder; differentiates from Python-centric benchmarks.
  • Consequence: Requires maintaining language-specific test case datasets, compilers,

Language runtime dependencies: evaluation assumes Python, Java (via javatuples-1.2.jar), C++, Go, JavaScript, Rust, etc. are installed and in PATH—missing toolchains cause silent failures. JSONL format is strict (missing 'canonical_solution' or 'test' keys breaks parsing). Timeout handling varies by language (Python uses signal, Java uses Thread.stop()—unreliable on some JVMs). Model download via HuggingFace requires internet and authentication for gated models. No explicit error recovery in execution.py—a single bad test case can crash the evaluator; recommend wrapping batch calls in try-catch.

• Pass@k metric — Core evaluation metric in code generation (pass@1, pass@10, pass@100); measures if at least 1 of k samples passes all test cases—essential to understand reported benchmark scores
• Fill-in-the-blank (FIB) / infilling task — DeepSeek Coder's 16K context window is trained with FIB tasks (predicting middle of code given prefix/suffix) to support project-level completion; affects how model handles longer contexts
• Functional correctness evaluation — Distinguishes code generation benchmarks from text metrics—evaluates via actual execution (not BLEU/token match) against hidden test cases; accounts for why execution sandboxing is complex
• Multi-language code pretraining corpus — DeepSeek Coder trained on 2T tokens (87% code, 13% language) across 80+ languages; corpus composition directly impacts model's cross-language generalization and benchmark coverage
• Execution sandbox / safe code evaluation — Critical for running untrusted model-generated code; execution.py isolates each test via timeouts, resource limits, and subprocess isolation to prevent hangs/crashes
• Canonical solution + test case structure — Benchmark JSONL format includes both ground-truth code (canonical_solution) and test assertions (test); allows both generation and validation of correctness

• openai/human-eval — Original HumanEval benchmark repo; DeepSeek Coder evaluation directly extends this dataset and metrics
• EleutherAI/lm-evaluation-harness — General-purpose LLM evaluation framework supporting code benchmarks; alternative for standardized multi-benchmark evaluation workflows
• meta-llama/codellama — Comparable open-source code LLM (Meta's alternative); both support similar benchmarks and multi-language generation
• bigcode-project/bigcode-evaluation-harness — BigCode's standardized evaluation for code models across HumanEval, MBPP, MultiPL-E; directly complementary benchmark suite
• deepseek-ai/DeepSeek-LLM — Parent repository for DeepSeek's general-purpose LLM series; code models are specialized variants of this lineage

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add unified evaluation harness and CI workflow for multi-language HumanEval benchmarks

The repo contains 17 language variants of HumanEval data (python, js, ts, java, cpp, etc.) in Evaluation/HumanEval/data/, but there's no automated CI pipeline to validate results across all languages. Currently only eval.sh exists without language-matrix testing. This would ensure code generation quality is maintained across all supported language targets and catch regressions early.

• [ ] Create .github/workflows/humaneval-multi-language.yml that runs Evaluation/HumanEval/eval.sh for each language variant (python, js, ts, java, cpp, cs, go, etc.)
• [ ] Modify Evaluation/HumanEval/eval.sh to accept language parameter and validate against corresponding humaneval-*.jsonl file
• [ ] Add CI config to test against sample models and baseline thresholds per language
• [ ] Document expected pass rates for each language in Evaluation/HumanEval/README.md

Consolidate duplicated human_eval modules across evaluation directories

The repo has 3 separate copies of nearly identical human_eval modules: Evaluation/HumanEval/human_eval/, Evaluation/LeetCode/human_eval/, and Evaluation/MBPP/human_eval/. These contain overlapping code (data.py, evaluation.py, execution.py) that violates DRY principles and creates maintenance burden. A shared module would reduce code duplication and ensure consistent evaluation logic.

• [ ] Create Evaluation/shared_eval/human_eval/ with consolidated data.py, execution.py, evaluation.py modules
• [ ] Audit differences between the three versions in Evaluation/HumanEval/human_eval/execution.py, Evaluation/LeetCode/human_eval/execution.py, and Evaluation/MBPP/human_eval/execution.py
• [ ] Merge implementations into shared module with language/dataset-specific parameters
• [ ] Update imports in Evaluation/HumanEval/humaneval.py, Evaluation/MBPP/eval_instruct.py, and Evaluation/LeetCode/evaluate_leetcode.py to use shared module
• [ ] Add unit tests in Evaluation/tests/test_shared_eval.py validating execution and evaluation consistency

Add dataset validation and schema documentation for evaluation data files

The repo has 20+ .jsonl evaluation dataset files across HumanEval, MBPP, and LeetCode without documented schemas or validation. Files like humaneval-python.jsonl, mbpp.jsonl lack specifications for required fields, making it hard for contributors to understand data format or add new datasets. This causes hidden bugs in data loading.

• [ ] Create Evaluation/data_schema.md documenting required fields for each dataset type (HumanEval format, MBPP format, LeetCode format) with example entries
• [ ] Create Evaluation/utils/validate_datasets.py with schema validators for each format using jsonschema or pydantic
• [ ] Add validation script to pre-commit hooks or CI to check all .jsonl files in Evaluation/*/data/ directories
• [ ] Update Evaluation/HumanEval/utils/dataset.py, Evaluation/MBPP/ data loading to use validators and provide clear error messages on schema violations

• Add unit tests for human_eval/execution.py language-specific execution handlers (currently untested); create tests/test_execution_java.py, test_execution_cpp.py with mock code snippets to verify timeout and error handling work correctly per language.
• Extend Evaluation/HumanEval/data/ with Kotlin, Swift, or Perl language datasets following the existing JSONL schema (task_id, prompt, entry_point, test, canonical_solution); coordinate with the paper's language coverage list.
• Document the YAML config schema in test_config.yaml (timeout values, compiler flags, runtime env vars) and add validation in humaneval.py to catch misconfigured languages early—currently missing language causes cryptic 'unknown executor' error.

• High · Outdated PyTorch Dependency — Dependencies/Package file (torch==2.0.1). torch==2.0.1 is pinned to a specific version from early 2024. This version may contain known security vulnerabilities. PyTorch has regular security updates and the current version is significantly outdated. Fix: Update to the latest stable PyTorch version (>=2.1.0) and implement a regular dependency update schedule. Use pip-audit or similar tools to identify known vulnerabilities.
• High · Outdated Transformers Library — Dependencies/Package file (transformers==4.35.0). transformers==4.35.0 is pinned to a version from September 2023. This version may contain security vulnerabilities related to model loading, tokenization, and input handling. Transformers library is frequently updated with security patches. Fix: Update to the latest stable transformers version (>=4.36.0) and establish a dependency management policy for security updates.
• High · Outdated DeepSpeed Dependency — Dependencies/Package file (deepspeed==0.12.2). deepspeed==0.12.2 is an older version that may contain security vulnerabilities. DeepSpeed is critical for distributed training and security updates are important. Fix: Update to the latest stable DeepSpeed version and implement automated security scanning for dependencies.
• Medium · Unconstrained Dependency Versions — Dependencies/Package file (attrdict, datasets, tqdm). Several dependencies lack version pinning or have overly loose constraints: 'attrdict' and 'datasets' have no version specified, 'tqdm' has no version constraint. This can lead to unexpected breaking changes or security issues from transitive dependencies. Fix: Pin all dependencies to specific versions or use version ranges (e.g., >=1.0.0,<2.0.0). Use a requirements.txt lock file with hashes for production deployments.
• Medium · Code Execution Risk in Evaluation Scripts — Evaluation/*/human_eval/execution.py, Evaluation/HumanEval/humaneval.py, Evaluation/MBPP/mbpp.py. The evaluation framework includes execution.py files in multiple evaluation directories (HumanEval, MBPP, LeetCode) that likely execute generated code. This could pose security risks if not properly sandboxed, allowing arbitrary code execution. Fix: Implement strict sandboxing for code execution using containers or restricted execution environments. Validate all generated code before execution. Use timeout mechanisms to prevent infinite loops.
• Medium · JSONL Data Files Without Validation — Evaluation/*/data/*.jsonl files. Multiple .jsonl evaluation data files are present without apparent input validation in the codebase structure. If these files are loaded directly without validation, malicious JSON could be injected. Fix: Implement strict JSON schema validation when loading JSONL files. Use a validation library like jsonschema. Verify data integrity and implement file integrity checks.
• Medium · Python Cache Files in Repository — Evaluation/HumanEval/__pycache__/, Evaluation/MBPP/__pycache__/, etc.. pycache directories are tracked in version control, indicating they may be committed. This can expose bytecode and source mapping information. Fix: Add pycache/ to .gitignore and .git/info/exclude. Remove cached bytecode from version history using git filter-branch or BFG Repo-Cleaner.
• Low · Missing Security Headers Configuration — Project root (missing Dockerfile, nginx config, etc.). No Docker or security configuration files visible in the provided structure. If this project is deployed as a web service, security headers may not be properly configured. Fix: If deploying as a service, implement proper security headers (CSP, X-Frame-Options, etc.). Use security scanning tools in CI/CD pipeline.
• Low · Hardcoded JAR File — Evaluation/HumanEval/javatuples-1. javatuples-1.2.jar is a binary file committed to the repository. Binary dependencies should be managed through package managers. Fix: undefined

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

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2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/deepseek-ai/DeepSeek-Coder shows verifiable citations alongside every claim.

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This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live deepseek-ai/DeepSeek-Coder repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/deepseek-ai/DeepSeek-Coder.

What it runs against: a local clone of deepseek-ai/DeepSeek-Coder — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in deepseek-ai/DeepSeek-Coder | Confirms the artifact applies here, not a fork | | 2 | License is still MIT | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 209 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of deepseek-ai/DeepSeek-Coder. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/deepseek-ai/DeepSeek-Coder.git
#   cd DeepSeek-Coder
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of deepseek-ai/DeepSeek-Coder and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "deepseek-ai/DeepSeek-Coder(\\.git)?\\b" \\
  && ok "origin remote is deepseek-ai/DeepSeek-Coder" \\
  || miss "origin remote is not deepseek-ai/DeepSeek-Coder (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(MIT)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"MIT\"" package.json 2>/dev/null) \\
  && ok "license is MIT" \\
  || miss "license drift — was MIT at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "Evaluation/HumanEval/humaneval.py" \\
  && ok "Evaluation/HumanEval/humaneval.py" \\
  || miss "missing critical file: Evaluation/HumanEval/humaneval.py"
test -f "Evaluation/HumanEval/human_eval/evaluate_functional_correctness.py" \\
  && ok "Evaluation/HumanEval/human_eval/evaluate_functional_correctness.py" \\
  || miss "missing critical file: Evaluation/HumanEval/human_eval/evaluate_functional_correctness.py"
test -f "Evaluation/HumanEval/human_eval/execution.py" \\
  && ok "Evaluation/HumanEval/human_eval/execution.py" \\
  || miss "missing critical file: Evaluation/HumanEval/human_eval/execution.py"
test -f "Evaluation/MBPP/mbpp.py" \\
  && ok "Evaluation/MBPP/mbpp.py" \\
  || miss "missing critical file: Evaluation/MBPP/mbpp.py"
test -f "Evaluation/LeetCode/evaluate_leetcode.py" \\
  && ok "Evaluation/LeetCode/evaluate_leetcode.py" \\
  || miss "missing critical file: Evaluation/LeetCode/evaluate_leetcode.py"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 209 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~179d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/deepseek-ai/DeepSeek-Coder"
  exit 1
fi

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