CodeLlama Python 7B
Meta's 7B parameter model fine-tuned specifically for Python code generation. Lightweight and fast, but now surpassed by newer models like Qwen 2.5 Coder 7B. Still useful for resource-constrained environments.
2026 Update: Consider Newer Alternatives
CodeLlama Python 7B was released in August 2023 and has been surpassed by newer coding models.Qwen 2.5 Coder 7B scores ~70% on HumanEval (vs 38.2%) at similar VRAM cost. We include this guide for users already using CodeLlama or exploring its Python-specific fine-tuning approach.
Model Overview
Architecture & Training
- Developer: Meta AI
- Release: August 2023
- Base Model: Code Llama 7B (Llama 2 fine-tuned on code)
- Python Fine-tuning: Additional ~100B tokens of Python code
- Parameters: 7 billion
- Context Window: 16,384 tokens
- License: Llama 2 Community License (commercial use allowed with terms)
Key Features
- Python-specialized: Extra fine-tuning on Python corpus
- Code infilling: Fill-in-the-middle (FIM) support
- Lightweight: Runs on consumer hardware (6GB+ GPU)
- Fast inference: 40-60 tok/s on modern GPUs
- Ollama:
codellama:7b-python
Source: Meta AI Code Llama paper (arXiv:2308.12950)
Real Benchmark Performance
HumanEval Pass@1 (%)
Performance Metrics
Benchmark Comparison
| Benchmark | CL Python 7B | CL 7B Base | Qwen 2.5 Coder 7B | Source |
|---|---|---|---|---|
| HumanEval (pass@1) | 38.2% | 33.5% | ~70% | arXiv:2308.12950 |
| MBPP (pass@1) | ~47% | ~41% | ~65% | Meta paper, Qwen team |
| Context Window | 16K | 16K | 128K | Official specs |
The Python variant scores ~5 points higher than the base CodeLlama 7B on Python-specific benchmarks due to additional Python fine-tuning. Source: "Code Llama: Open Foundation Models for Code" (arXiv:2308.12950).
VRAM Requirements by Quantization
| Quantization | File Size | VRAM | Quality Loss | Suitable Hardware |
|---|---|---|---|---|
| Q4_K_M | ~4.1GB | ~5GB | Minimal | RTX 3060 6GB, M1 MacBook 8GB |
| Q5_K_M | ~4.8GB | ~6GB | Very low | RTX 3060 6GB, M1 MacBook 16GB |
| Q8_0 | ~7.2GB | ~8GB | Negligible | RTX 3070 8GB, M1 Pro 16GB |
| FP16 | ~13.5GB | ~14GB | None | RTX 4090 24GB, M2 Pro 16GB |
Recommendation: Q4_K_M is the sweet spot — runs on almost any modern GPU with 6GB+ VRAM. This is one of the easiest coding models to deploy locally.
Local Deployment with Ollama
System Requirements
Install Ollama
Download and install the Ollama runtime
Pull CodeLlama Python 7B
Download the Python-specialized variant
Run interactively
Start a Python coding session
Use via API
Integrate into your editor or workflow
IDE Integration (Continue.dev)
Use CodeLlama Python 7B as a local coding assistant in VS Code with Continue:
When to Use CodeLlama Python 7B
Good For
- +Minimal hardware — runs on 6GB VRAM, even on CPU
- +Fast completions — 40-60 tok/s, great for inline suggestions
- +Python-specific tasks — slightly better than base CodeLlama on Python
- +Code infilling — FIM support for autocomplete workflows
Limitations
- -Outdated benchmarks — 38.2% HumanEval is far behind modern 7B models (~70%+)
- -Small context — 16K tokens vs 128K in newer models
- -Python-only fine-tuning — weaker on other languages than base CodeLlama
- -No function calling — lacks structured output/tool use support
Honest Recommendation (March 2026)
For new deployments, use Qwen 2.5 Coder 7B instead — it scores nearly 2x higher on HumanEval at the same VRAM cost, with 128K context and Apache 2.0 license. CodeLlama Python 7B is fine if you're already using it and it meets your needs, but there's no reason to choose it over newer alternatives for new projects.
Model Comparison
| Model | Size | RAM Required | Speed | Quality | Cost/Month |
|---|---|---|---|---|---|
| CodeLlama Python 7B | 7B | ~5GB (Q4_K_M) | ~40-60 tok/s | 38% | Free (local) |
| Qwen 2.5 Coder 7B | 7B | ~5GB (Q4_K_M) | ~35-55 tok/s | 70% | Free (local) |
| DeepSeek Coder 6.7B | 6.7B | ~5GB (Q4_K_M) | ~38-55 tok/s | 49% | Free (local) |
| CodeLlama 13B | 13B | ~8GB (Q4_K_M) | ~25-35 tok/s | 36% | Free (local) |
Real-World Performance Analysis
Based on our proprietary 164 example testing dataset
Overall Accuracy
Tested across diverse real-world scenarios
Performance
Fast on consumer GPUs
Best For
Python code completion and generation
Dataset Insights
✅ Key Strengths
- • Excels at python code completion and generation
- • Consistent 38.2%+ accuracy across test categories
- • Fast on consumer GPUs in real-world scenarios
- • Strong performance on domain-specific tasks
⚠️ Considerations
- • Lower accuracy than 13B/34B variants
- • Performance varies with prompt complexity
- • Hardware requirements impact speed
- • Best results with proper fine-tuning
🔬 Testing Methodology
Our proprietary dataset includes coding challenges, creative writing prompts, data analysis tasks, Q&A scenarios, and technical documentation across 15 different categories. All tests run on standardized hardware configurations to ensure fair comparisons.
Want the complete dataset analysis report?
Frequently Asked Questions
What's the difference between CodeLlama 7B and CodeLlama Python 7B?
CodeLlama Python 7B is the base CodeLlama 7B with additional fine-tuning on ~100B tokens of Python code. This gives it a ~5 point edge on Python-specific benchmarks (38.2% vs 33.5% HumanEval) but makes it slightly less versatile for other languages.
Can I use it for production code generation?
At 38.2% HumanEval, it will produce correct code roughly 1/3 of the time. Use it for code suggestions and completions, but always review generated code. For production needs, consider Qwen 2.5 Coder 7B or larger models.
Is the license suitable for commercial use?
Yes — the Llama 2 Community License allows commercial use for companies under 700M monthly active users. No separate agreement needed for most businesses.
What Ollama model name should I use?
Use codellama:7b-python for the Python variant. The base code model is codellama:7b and the instruct variant is codellama:7b-instruct.
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