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README.md

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+---
+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](https://github.com/kevco/kode), an open-source local alternative to Claude Code.
+
+| 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