---
language:
  - en
  - de
license: apache-2.0
library_name: transformers
base_model:
  - Qwen/Qwen2.5-Coder-14B
  - Qwen/Qwen2.5-Coder-32B
tags:
  - code
  - coding
  - tool-calling
  - code-generation
  - eu-trained
  - dpo
  - sft
  - qlora
pipeline_tag: text-generation
model-index:
  - name: Kode
    results: []
---

# Kode — EU-Trained Coding Models

**Kode** is a family of instruction-tuned coding models built for real-world software engineering tasks. Fine-tuned on **Qwen2.5-Coder** using DPO + SFT with Claude-generated training samples on A100 GPUs.

Kode is the backbone of Kode CLI/Web UI, an open-source local alternative to Claude Code. Github coming soon.

| Model | Parameters | VRAM | Best For |
|-------|-----------|------|----------|
| **kode-14b** | 14B | ~10 GB (Q8) / ~9 GB (Q4) | Consumer GPUs, fast iteration |
| **kode-32b** | 32B | ~19 GB (Q4) | Maximum quality, production use |

## Key Features

- 🇪🇺 **Trained in the EU** — DSGVO/GDPR compliant, no data leaves Europe
- 🔧 **Tool-calling native** — Trained specifically for file operations, shell commands, code search
- 🎯 **Production code focus** — Training data from real codebases, not synthetic benchmarks
- 📐 **7 languages** — Rust, Go, TypeScript, Python, C#, SQL, CSS/Tailwind
- 🏠 **Runs locally** — 14B fits on a single consumer GPU (RTX 3080+)

## Supported Languages & Tasks

### Languages
Rust • Go • TypeScript • Python • C# • PostgreSQL • CSS/Tailwind

### Tasks
- **Code generation** — Complete functions, modules, and files from natural language
- **Code refactoring** — Improve existing code structure and performance
- **Code review** — Identify bugs, security issues, and improvements
- **Tool calling** — File I/O, shell commands, grep/search (Kode CLI integration)
- **Code completion** — Context-aware completions

## Training Details

### Base Model
[Qwen2.5-Coder](https://huggingface.co/Qwen/Qwen2.5-Coder-32B) (14B and 32B variants)

### Training Pipeline
1. **SFT (Supervised Fine-Tuning)** — Claude-generated training samples across 7 languages (~841 curated queries covering data structures, async, error handling, APIs, testing, and more)
2. **DPO (Direct Preference Optimization)** — Preference pairs from Claude evaluations of model outputs
3. **Tool-call SFT** — Specialized training for tool-calling patterns (read_file, write_file, bash_execute, grep, etc.)

### Infrastructure
- **GPU:** NVIDIA A100 80GB (2× for 32B full fine-tune, 1× for QLoRA)
- **Framework:** Transformers + PEFT + TRL + Unsloth
- **LoRA config (32B):** r=64, alpha=128, dropout=0.05, targeting all attention + MLP projections
- **Precision:** bfloat16
- **Sequence length:** 4096 tokens

### Training Data
- ~841 curated training queries across 7 programming languages
- Claude-generated reference solutions (chosen) vs. local model outputs (rejected) for DPO
- Bilingual prompts (English + German)

## Usage

### Ollama (Recommended)

```bash
# Install and run
ollama pull simplellm/kode-14b
ollama run simplellm/kode-14b

# Or the larger model
ollama pull simplellm/kode-32b
ollama run simplellm/kode-32b
```

### Ollama API

```bash
curl http://localhost:11434/api/chat -d '{
  "model": "simplellm/kode-14b",
  "messages": [
    {"role": "user", "content": "Write a Rust function to find prime numbers using the Sieve of Eratosthenes"}
  ]
}'
```

### 🤗 Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "simplellm/kode-14b"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "system", "content": "You are a coding assistant. Respond with clean, production-ready code."},
    {"role": "user", "content": "Write a thread-safe LRU cache in Rust using Arc and Mutex"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
```

### llama.cpp

```bash
# Download GGUF
wget https://huggingface.co/simplellm/kode-14b-GGUF/resolve/main/kode-14b-Q8_0.gguf

# Run
./llama-cli -m kode-14b-Q8_0.gguf -p "Write a Go HTTP server with middleware" -n 1024
```

### Hosted Inference

Try Kode without downloading at **[SimpleLLM.eu](https://simplellm.eu)** — EU-hosted, GDPR-compliant inference API.

## Quantized Versions

| Variant | Size | Quality | Speed |
|---------|------|---------|-------|
| kode-14b (FP16) | ~28 GB | Baseline | Baseline |
| kode-14b-Q8 | ~15 GB | Near-lossless | ~1.2× faster |
| kode-14b (Q4) | ~9 GB | Good | ~1.5× faster |
| kode-32b (native/FP16) | ~64 GB | Best | Slowest |
| kode-32b-Q4 | ~19 GB | Very good | Fast |

## Benchmarks

> 🚧 **Coming soon** — We are running HumanEval, MBPP, MultiPL-E, and tool-calling benchmarks. Results will be published here.

| Benchmark | kode-14b | kode-32b | Qwen2.5-Coder-14B (base) |
|-----------|----------|----------|--------------------------|
| HumanEval | TBD | TBD | TBD |
| MBPP | TBD | TBD | TBD |
| MultiPL-E (Rust) | TBD | TBD | TBD |
| Tool-call accuracy | TBD | TBD | N/A |

## Limitations

- Optimized for the 7 supported languages; may underperform on others
- 4096 token context window (inherited from training config)
- Tool-calling format is specific to Kode CLI's tool schema
- Training data is bilingual (EN/DE) — other languages may have reduced quality

## License

Apache 2.0 (inherited from [Qwen2.5-Coder](https://huggingface.co/Qwen/Qwen2.5-Coder-32B))

## Citation

```bibtex
@misc{kode2025,
  title={Kode: EU-Trained Coding Models for Real-World Software Engineering},
  author={Kevin and SimpleLLM Team},
  year={2025},
  url={https://huggingface.co/simplellm/kode-14b}
}
```

## Links

- 🌐 [SimpleLLM.eu](https://simplellm.eu) — Hosted inference
- 💻 [Kode CLI](https://github.com/kevco/kode) — Local coding assistant
- 🤗 [All models](https://huggingface.co/simplellm) — HuggingFace collection