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evaluating-code-models

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project

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BigCode Evaluation Harness - Code Model Benchmarking

Quick Start

BigCode Evaluation Harness evaluates code generation models across 15+ benchmarks including HumanEval, MBPP, and MultiPL-E (18 languages).

Installation:

git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
cd bigcode-evaluation-harness
pip install -e .
accelerate config

Evaluate on HumanEval:

accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --max_length_generation 512 \
  --temperature 0.2 \
  --n_samples 20 \
  --batch_size 10 \
  --allow_code_execution \
  --save_generations

View available tasks:

python -c "from bigcode_eval.tasks import ALL_TASKS; print(ALL_TASKS)"

Common Workflows

Workflow 1: Standard Code Benchmark Evaluation

Evaluate model on core code benchmarks (HumanEval, MBPP, HumanEval+).

Checklist:

Code Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model and generation
- [ ] Step 3: Run evaluation with code execution
- [ ] Step 4: Analyze pass@k results

Step 1: Choose benchmark suite

Python code generation (most common):

HumanEval: 164 handwritten problems, function completion
HumanEval+: Same 164 problems with 80× more tests (stricter)
MBPP: 500 crowd-sourced problems, entry-level difficulty
MBPP+: 399 curated problems with 35× more tests

Multi-language (18 languages):

MultiPL-E: HumanEval/MBPP translated to C++, Java, JavaScript, Go, Rust, etc.

Advanced:

APPS: 10,000 problems (introductory/interview/competition)
DS-1000: 1,000 data science problems across 7 libraries

Step 2: Configure model and generation

# Standard HuggingFace model
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --max_length_generation 512 \
  --temperature 0.2 \
  --do_sample True \
  --n_samples 200 \
  --batch_size 50 \
  --allow_code_execution

# Quantized model (4-bit)
accelerate launch main.py \
  --model codellama/CodeLlama-34b-hf \
  --tasks humaneval \
  --load_in_4bit \
  --max_length_generation 512 \
  --allow_code_execution

# Custom/private model
accelerate launch main.py \
  --model /path/to/my-code-model \
  --tasks humaneval \
  --trust_remote_code \
  --use_auth_token \
  --allow_code_execution

Step 3: Run evaluation

# Full evaluation with pass@k estimation (k=1,10,100)
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --temperature 0.8 \
  --n_samples 200 \
  --batch_size 50 \
  --allow_code_execution \
  --save_generations \
  --metric_output_path results/starcoder2-humaneval.json

Step 4: Analyze results

Results in results/starcoder2-humaneval.json:

{
  "humaneval": {
    "pass@1": 0.354,
    "pass@10": 0.521,
    "pass@100": 0.689
  },
  "config": {
    "model": "bigcode/starcoder2-7b",
    "temperature": 0.8,
    "n_samples": 200
  }
}

Workflow 2: Multi-Language Evaluation (MultiPL-E)

Evaluate code generation across 18 programming languages.

Checklist:

Multi-Language Evaluation:
- [ ] Step 1: Generate solutions (host machine)
- [ ] Step 2: Run evaluation in Docker (safe execution)
- [ ] Step 3: Compare across languages

Step 1: Generate solutions on host

# Generate without execution (safe)
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks multiple-py,multiple-js,multiple-java,multiple-cpp \
  --max_length_generation 650 \
  --temperature 0.8 \
  --n_samples 50 \
  --batch_size 50 \
  --generation_only \
  --save_generations \
  --save_generations_path generations_multi.json

Step 2: Evaluate in Docker container

# Pull the MultiPL-E Docker image
docker pull ghcr.io/bigcode-project/evaluation-harness-multiple

# Run evaluation inside container
docker run -v $(pwd)/generations_multi.json:/app/generations.json:ro \
  -it evaluation-harness-multiple python3 main.py \
  --model bigcode/starcoder2-7b \
  --tasks multiple-py,multiple-js,multiple-java,multiple-cpp \
  --load_generations_path /app/generations.json \
  --allow_code_execution \
  --n_samples 50

Supported languages: Python, JavaScript, Java, C++, Go, Rust, TypeScript, C#, PHP, Ruby, Swift, Kotlin, Scala, Perl, Julia, Lua, R, Racket

Workflow 3: Instruction-Tuned Model Evaluation

Evaluate chat/instruction models with proper formatting.

Checklist:

Instruction Model Evaluation:
- [ ] Step 1: Use instruction-tuned tasks
- [ ] Step 2: Configure instruction tokens
- [ ] Step 3: Run evaluation

Step 1: Choose instruction tasks

instruct-humaneval: HumanEval with instruction prompts
humanevalsynthesize-{lang}: HumanEvalPack synthesis tasks

Step 2: Configure instruction tokens

# For models with chat templates (e.g., CodeLlama-Instruct)
accelerate launch main.py \
  --model codellama/CodeLlama-7b-Instruct-hf \
  --tasks instruct-humaneval \
  --instruction_tokens "<s>[INST],</s>,[/INST]" \
  --max_length_generation 512 \
  --allow_code_execution

Step 3: HumanEvalPack for instruction models

# Test code synthesis across 6 languages
accelerate launch main.py \
  --model codellama/CodeLlama-7b-Instruct-hf \
  --tasks humanevalsynthesize-python,humanevalsynthesize-js \
  --prompt instruct \
  --max_length_generation 512 \
  --allow_code_execution

Workflow 4: Compare Multiple Models

Benchmark suite for model comparison.

Step 1: Create evaluation script

#!/bin/bash
# eval_models.sh

MODELS=(
  "bigcode/starcoder2-7b"
  "codellama/CodeLlama-7b-hf"
  "deepseek-ai/deepseek-coder-6.7b-base"
)
TASKS="humaneval,mbpp"

for model in "${MODELS[@]}"; do
  model_name=$(echo $model | tr '/' '-')
  echo "Evaluating $model"

  accelerate launch main.py \
    --model $model \
    --tasks $TASKS \
    --temperature 0.2 \
    --n_samples 20 \
    --batch_size 20 \
    --allow_code_execution \
    --metric_output_path results/${model_name}.json
done

Step 2: Generate comparison table

import json
import pandas as pd

models = ["bigcode-starcoder2-7b", "codellama-CodeLlama-7b-hf", "deepseek-ai-deepseek-coder-6.7b-base"]
results = []

for model in models:
    with open(f"results/{model}.json") as f:
        data = json.load(f)
        results.append({
            "Model": model,
            "HumanEval pass@1": f"{data['humaneval']['pass@1']:.3f}",
            "MBPP pass@1": f"{data['mbpp']['pass@1']:.3f}"
        })

df = pd.DataFrame(results)
print(df.to_markdown(index=False))

When to Use vs Alternatives

Use BigCode Evaluation Harness when:

Evaluating code generation models specifically
Need multi-language evaluation (18 languages via MultiPL-E)
Testing functional correctness with unit tests (pass@k)
Benchmarking for BigCode/HuggingFace leaderboards
Evaluating fill-in-the-middle (FIM) capabilities

Use alternatives instead:

lm-evaluation-harness: General LLM benchmarks (MMLU, GSM8K, HellaSwag)
EvalPlus: Stricter HumanEval+/MBPP+ with more test cases
SWE-bench: Real-world GitHub issue resolution
LiveCodeBench: Contamination-free, continuously updated problems
CodeXGLUE: Code understanding tasks (clone detection, defect prediction)

Supported Benchmarks

Benchmark	Problems	Languages	Metric	Use Case
HumanEval	164	Python	pass@k	Standard code completion
HumanEval+	164	Python	pass@k	Stricter evaluation (80× tests)
MBPP	500	Python	pass@k	Entry-level problems
MBPP+	399	Python	pass@k	Stricter evaluation (35× tests)
MultiPL-E	164×18	18 languages	pass@k	Multi-language evaluation
APPS	10,000	Python	pass@k	Competition-level
DS-1000	1,000	Python	pass@k	Data science (pandas, numpy, etc.)
HumanEvalPack	164×3×6	6 languages	pass@k	Synthesis/fix/explain
Mercury	1,889	Python	Efficiency	Computational efficiency

Common Issues

Issue: Different results than reported in papers

Check these factors:

# 1. Verify n_samples (need 200 for accurate pass@k)
--n_samples 200

# 2. Check temperature (0.2 for greedy-ish, 0.8 for sampling)
--temperature 0.8

# 3. Verify task name matches exactly
--tasks humaneval  # Not "human_eval" or "HumanEval"

# 4. Check max_length_generation
--max_length_generation 512  # Increase for longer problems

Issue: CUDA out of memory

# Use quantization
--load_in_8bit
# OR
--load_in_4bit

# Reduce batch size
--batch_size 1

# Set memory limit
--max_memory_per_gpu "20GiB"

Issue: Code execution hangs or times out

Use Docker for safe execution:

# Generate on host (no execution)
--generation_only --save_generations

# Evaluate in Docker
docker run ... --allow_code_execution --load_generations_path ...

Issue: Low scores on instruction models

Ensure proper instruction formatting:

# Use instruction-specific tasks
--tasks instruct-humaneval

# Set instruction tokens for your model
--instruction_tokens "<s>[INST],</s>,[/INST]"

Issue: MultiPL-E language failures

Use the dedicated Docker image:

docker pull ghcr.io/bigcode-project/evaluation-harness-multiple

Command Reference

Argument	Default	Description
`--model`	-	HuggingFace model ID or local path
`--tasks`	-	Comma-separated task names
`--n_samples`	1	Samples per problem (200 for pass@k)
`--temperature`	0.2	Sampling temperature
`--max_length_generation`	512	Max tokens (prompt + generation)
`--batch_size`	1	Batch size per GPU
`--allow_code_execution`	False	Enable code execution (required)
`--generation_only`	False	Generate without evaluation
`--load_generations_path`	-	Load pre-generated solutions
`--save_generations`	False	Save generated code
`--metric_output_path`	results.json	Output file for metrics
`--load_in_8bit`	False	8-bit quantization
`--load_in_4bit`	False	4-bit quantization
`--trust_remote_code`	False	Allow custom model code
`--precision`	fp32	Model precision (fp32/fp16/bf16)

Hardware Requirements

Model Size	VRAM (fp16)	VRAM (4-bit)	Time (HumanEval, n=200)
7B	14GB	6GB	~30 min (A100)
13B	26GB	10GB	~1 hour (A100)
34B	68GB	20GB	~2 hours (A100)

Resources

GitHub: https://github.com/bigcode-project/bigcode-evaluation-harness
Documentation: https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main/docs
BigCode Leaderboard: https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard
HumanEval Dataset: https://huggingface.co/datasets/openai/openai_humaneval
MultiPL-E: https://github.com/nuprl/MultiPL-E

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Metadatos del archivo

name: evaluating-code-models
description: Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Evaluation, Code Generation, HumanEval, MBPP, MultiPL-E, Pass@k, BigCode, Benchmarking, Code Models]
dependencies: [bigcode-evaluation-harness, transformers>=4.25.1, accelerate>=0.13.2, datasets>=2.6.1]

Ver texto original

---
name: evaluating-code-models
description: Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Evaluation, Code Generation, HumanEval, MBPP, MultiPL-E, Pass@k, BigCode, Benchmarking, Code Models]
dependencies: [bigcode-evaluation-harness, transformers>=4.25.1, accelerate>=0.13.2, datasets>=2.6.1]
---

# BigCode Evaluation Harness - Code Model Benchmarking

## Quick Start

BigCode Evaluation Harness evaluates code generation models across 15+ benchmarks including HumanEval, MBPP, and MultiPL-E (18 languages).

**Installation**:
```bash
git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
cd bigcode-evaluation-harness
pip install -e .
accelerate config
```

**Evaluate on HumanEval**:
```bash
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --max_length_generation 512 \
  --temperature 0.2 \
  --n_samples 20 \
  --batch_size 10 \
  --allow_code_execution \
  --save_generations
```

**View available tasks**:
```bash
python -c "from bigcode_eval.tasks import ALL_TASKS; print(ALL_TASKS)"
```

## Common Workflows

### Workflow 1: Standard Code Benchmark Evaluation

Evaluate model on core code benchmarks (HumanEval, MBPP, HumanEval+).

**Checklist**:
```
Code Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model and generation
- [ ] Step 3: Run evaluation with code execution
- [ ] Step 4: Analyze pass@k results
```

**Step 1: Choose benchmark suite**

**Python code generation** (most common):
- **HumanEval**: 164 handwritten problems, function completion
- **HumanEval+**: Same 164 problems with 80× more tests (stricter)
- **MBPP**: 500 crowd-sourced problems, entry-level difficulty
- **MBPP+**: 399 curated problems with 35× more tests

**Multi-language** (18 languages):
- **MultiPL-E**: HumanEval/MBPP translated to C++, Java, JavaScript, Go, Rust, etc.

**Advanced**:
- **APPS**: 10,000 problems (introductory/interview/competition)
- **DS-1000**: 1,000 data science problems across 7 libraries

**Step 2: Configure model and generation**

```bash
# Standard HuggingFace model
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --max_length_generation 512 \
  --temperature 0.2 \
  --do_sample True \
  --n_samples 200 \
  --batch_size 50 \
  --allow_code_execution

# Quantized model (4-bit)
accelerate launch main.py \
  --model codellama/CodeLlama-34b-hf \
  --tasks humaneval \
  --load_in_4bit \
  --max_length_generation 512 \
  --allow_code_execution

# Custom/private model
accelerate launch main.py \
  --model /path/to/my-code-model \
  --tasks humaneval \
  --trust_remote_code \
  --use_auth_token \
  --allow_code_execution
```

**Step 3: Run evaluation**

```bash
# Full evaluation with pass@k estimation (k=1,10,100)
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --temperature 0.8 \
  --n_samples 200 \
  --batch_size 50 \
  --allow_code_execution \
  --save_generations \
  --metric_output_path results/starcoder2-humaneval.json
```

**Step 4: Analyze results**

Results in `results/starcoder2-humaneval.json`:
```json
{
  "humaneval": {
    "pass@1": 0.354,
    "pass@10": 0.521,
    "pass@100": 0.689
  },
  "config": {
    "model": "bigcode/starcoder2-7b",
    "temperature": 0.8,
    "n_samples": 200
  }
}
```

### Workflow 2: Multi-Language Evaluation (MultiPL-E)

Evaluate code generation across 18 programming languages.

**Checklist**:
```
Multi-Language Evaluation:
- [ ] Step 1: Generate solutions (host machine)
- [ ] Step 2: Run evaluation in Docker (safe execution)
- [ ] Step 3: Compare across languages
```

**Step 1: Generate solutions on host**

```bash
# Generate without execution (safe)
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks multiple-py,multiple-js,multiple-java,multiple-cpp \
  --max_length_generation 650 \
  --temperature 0.8 \
  --n_samples 50 \
  --batch_size 50 \
  --generation_only \
  --save_generations \
  --save_generations_path generations_multi.json
```

**Step 2: Evaluate in Docker container**

```bash
# Pull the MultiPL-E Docker image
docker pull ghcr.io/bigcode-project/evaluation-harness-multiple

# Run evaluation inside container
docker run -v $(pwd)/generations_multi.json:/app/generations.json:ro \
  -it evaluation-harness-multiple python3 main.py \
  --model bigcode/starcoder2-7b \
  --tasks multiple-py,multiple-js,multiple-java,multiple-cpp \
  --load_generations_path /app/generations.json \
  --allow_code_execution \
  --n_samples 50
```

**Supported languages**: Python, JavaScript, Java, C++, Go, Rust, TypeScript, C#, PHP, Ruby, Swift, Kotlin, Scala, Perl, Julia, Lua, R, Racket

### Workflow 3: Instruction-Tuned Model Evaluation

Evaluate chat/instruction models with proper formatting.

**Checklist**:
```
Instruction Model Evaluation:
- [ ] Step 1: Use instruction-tuned tasks
- [ ] Step 2: Configure instruction tokens
- [ ] Step 3: Run evaluation
```

**Step 1: Choose instruction tasks**

- **instruct-humaneval**: HumanEval with instruction prompts
- **humanevalsynthesize-{lang}**: HumanEvalPack synthesis tasks

**Step 2: Configure instruction tokens**

```bash
# For models with chat templates (e.g., CodeLlama-Instruct)
accelerate launch main.py \
  --model codellama/CodeLlama-7b-Instruct-hf \
  --tasks instruct-humaneval \
  --instruction_tokens "<s>[INST],</s>,[/INST]" \
  --max_length_generation 512 \
  --allow_code_execution
```

**Step 3: HumanEvalPack for instruction models**

```bash
# Test code synthesis across 6 languages
accelerate launch main.py \
  --model codellama/CodeLlama-7b-Instruct-hf \
  --tasks humanevalsynthesize-python,humanevalsynthesize-js \
  --prompt instruct \
  --max_length_generation 512 \
  --allow_code_execution
```

### Workflow 4: Compare Multiple Models

Benchmark suite for model comparison.

**Step 1: Create evaluation script**

```bash
#!/bin/bash
# eval_models.sh

MODELS=(
  "bigcode/starcoder2-7b"
  "codellama/CodeLlama-7b-hf"
  "deepseek-ai/deepseek-coder-6.7b-base"
)
TASKS="humaneval,mbpp"

for model in "${MODELS[@]}"; do
  model_name=$(echo $model | tr '/' '-')
  echo "Evaluating $model"

  accelerate launch main.py \
    --model $model \
    --tasks $TASKS \
    --temperature 0.2 \
    --n_samples 20 \
    --batch_size 20 \
    --allow_code_execution \
    --metric_output_path results/${model_name}.json
done
```

**Step 2: Generate comparison table**

```python
import json
import pandas as pd

models = ["bigcode-starcoder2-7b", "codellama-CodeLlama-7b-hf", "deepseek-ai-deepseek-coder-6.7b-base"]
results = []

for model in models:
    with open(f"results/{model}.json") as f:
        data = json.load(f)
        results.append({
            "Model": model,
            "HumanEval pass@1": f"{data['humaneval']['pass@1']:.3f}",
            "MBPP pass@1": f"{data['mbpp']['pass@1']:.3f}"
        })

df = pd.DataFrame(results)
print(df.to_markdown(index=False))
```

## When to Use vs Alternatives

**Use BigCode Evaluation Harness when:**
- Evaluating **code generation** models specifically
- Need **multi-language** evaluation (18 languages via MultiPL-E)
- Testing **functional correctness** with unit tests (pass@k)
- Benchmarking for **BigCode/HuggingFace leaderboards**
- Evaluating **fill-in-the-middle** (FIM) capabilities

**Use alternatives instead:**
- **lm-evaluation-harness**: General LLM benchmarks (MMLU, GSM8K, HellaSwag)
- **EvalPlus**: Stricter HumanEval+/MBPP+ with more test cases
- **SWE-bench**: Real-world GitHub issue resolution
- **LiveCodeBench**: Contamination-free, continuously updated problems
- **CodeXGLUE**: Code understanding tasks (clone detection, defect prediction)

## Supported Benchmarks

| Benchmark | Problems | Languages | Metric | Use Case |
|-----------|----------|-----------|--------|----------|
| HumanEval | 164 | Python | pass@k | Standard code completion |
| HumanEval+ | 164 | Python | pass@k | Stricter evaluation (80× tests) |
| MBPP | 500 | Python | pass@k | Entry-level problems |
| MBPP+ | 399 | Python | pass@k | Stricter evaluation (35× tests) |
| MultiPL-E | 164×18 | 18 languages | pass@k | Multi-language evaluation |
| APPS | 10,000 | Python | pass@k | Competition-level |
| DS-1000 | 1,000 | Python | pass@k | Data science (pandas, numpy, etc.) |
| HumanEvalPack | 164×3×6 | 6 languages | pass@k | Synthesis/fix/explain |
| Mercury | 1,889 | Python | Efficiency | Computational efficiency |

## Common Issues

**Issue: Different results than reported in papers**

Check these factors:
```bash
# 1. Verify n_samples (need 200 for accurate pass@k)
--n_samples 200

# 2. Check temperature (0.2 for greedy-ish, 0.8 for sampling)
--temperature 0.8

# 3. Verify task name matches exactly
--tasks humaneval  # Not "human_eval" or "HumanEval"

# 4. Check max_length_generation
--max_length_generation 512  # Increase for longer problems
```

**Issue: CUDA out of memory**

```bash
# Use quantization
--load_in_8bit
# OR
--load_in_4bit

# Reduce batch size
--batch_size 1

# Set memory limit
--max_memory_per_gpu "20GiB"
```

**Issue: Code execution hangs or times out**

Use Docker for safe execution:
```bash
# Generate on host (no execution)
--generation_only --save_generations

# Evaluate in Docker
docker run ... --allow_code_execution --load_generations_path ...
```

**Issue: Low scores on instruction models**

Ensure proper instruction formatting:
```bash
# Use instruction-specific tasks
--tasks instruct-humaneval

# Set instruction tokens for your model
--instruction_tokens "<s>[INST],</s>,[/INST]"
```

**Issue: MultiPL-E language failures**

Use the dedicated Docker image:
```bash
docker pull ghcr.io/bigcode-project/evaluation-harness-multiple
```

## Command Reference

| Argument | Default | Description |
|----------|---------|-------------|
| `--model` | - | HuggingFace model ID or local path |
| `--tasks` | - | Comma-separated task names |
| `--n_samples` | 1 | Samples per problem (200 for pass@k) |
| `--temperature` | 0.2 | Sampling temperature |
| `--max_length_generation` | 512 | Max tokens (prompt + generation) |
| `--batch_size` | 1 | Batch size per GPU |
| `--allow_code_execution` | False | Enable code execution (required) |
| `--generation_only` | False | Generate without evaluation |
| `--load_generations_path` | - | Load pre-generated solutions |
| `--save_generations` | False | Save generated code |
| `--metric_output_path` | results.json | Output file for metrics |
| `--load_in_8bit` | False | 8-bit quantization |
| `--load_in_4bit` | False | 4-bit quantization |
| `--trust_remote_code` | False | Allow custom model code |
| `--precision` | fp32 | Model precision (fp32/fp16/bf16) |

## Hardware Requirements

| Model Size | VRAM (fp16) | VRAM (4-bit) | Time (HumanEval, n=200) |
|------------|-------------|--------------|-------------------------|
| 7B | 14GB | 6GB | ~30 min (A100) |
| 13B | 26GB | 10GB | ~1 hour (A100) |
| 34B | 68GB | 20GB | ~2 hours (A100) |

## Resources

- **GitHub**: https://github.com/bigcode-project/bigcode-evaluation-harness
- **Documentation**: https://github.com/bigcode-project/bigcode-evaluation-harness/tree/main/docs
- **BigCode Leaderboard**: https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard
- **HumanEval Dataset**: https://huggingface.co/datasets/openai/openai_humaneval
- **MultiPL-E**: https://github.com/nuprl/MultiPL-E

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Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.

Fuente y notas de uso

Indexado

Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.

Repositorio fuente: sangrokjung/claude-forge
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 3 sept 2026
Registro actualizado: 5 sept 2026
Ruta de instrucciones: skills/evaluating-code-models/SKILL.md @ 34d881dc9bdc

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

77/100

Sólido

Confianza

67/100

Solo sandbox

Auditoría

80/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Resultados: —

Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.

Acceso para agentes

La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

Abrir API de instalación Formato de texto LLM Abrir Manifest Plan de resolución automática

Más detalles

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Creador: Orchestra Research
Fuente: sangrokjung/claude-forge
Indexado por: Índice comunitario de OpenAgentSkill

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Resumen

BigCode Evaluation Harness - Code Model Benchmarking

Quick Start

Common Workflows

Workflow 1: Standard Code Benchmark Evaluation

Workflow 2: Multi-Language Evaluation (MultiPL-E)

Workflow 3: Instruction-Tuned Model Evaluation

Workflow 4: Compare Multiple Models

When to Use vs Alternatives

Supported Benchmarks

Common Issues

Command Reference

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