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| 🎯 Task | CLI error → single bare bash fix command |
| 🏆 Accuracy | 71.1% pipeline×3 · 42.2% raw LLM |
| 💾 Size | ~941MB Q4_K_M GGUF · ~2GB RAM |
| ⚡ Speed | <1s on Apple Silicon |
| 🔒 Privacy | 100% local · no API key · no telemetry |
1ollama run hf.co/erdemozkan/YOLO-Coder-1.5B "ModuleNotFoundError: No module named 'flask'"
2# → pip install flaskYOLO-Coder-8B pipeline×3 ████████████████████ 77.1%
YOLO-Coder-1.5B pipeline×3 ██████████████████ 71.1% ★ this model
Claude Sonnet raw ████████████████ 60.1%
YOLO-Coder-8B raw ███████████████ 59.2%
GPT-4o raw ████████████ 48.6%
YOLO-Coder-1.5B raw ██████████ 42.2%| Mode | Structural Match |
|---|---|
| Raw LLM (no pipeline) | 42.2% |
| Pipeline × 1 (interceptors + LLM) | 66.5% |
| Pipeline × 3 (interceptors + memory + 3 LLM attempts) | 71.1% |
At ~941MB, YOLO-Coder-1.5B reaches 71.1% with the full pipeline — running entirely offline.
Your error → [91 interceptors <1ms] → [fix memory <5ms] → [LLM <1s] → Fix
↑ ~50% of fixes stop here1pip install yolo-coder
2
3yoco --model hf.co/erdemozkan/YOLO-Coder-1.5B python3 myapp.py
4yoco --model hf.co/erdemozkan/YOLO-Coder-1.5B npm run dev<|im_start|>system
You are a CLI repair tool. Output ONLY a single bare bash command to fix the error. No explanation. No markdown. No backticks.<|im_end|>
<|im_start|>user
[Linux] $ python3 myapp.py
Error:
ModuleNotFoundError: No module named 'requests'
FIX:<|im_end|>
<|im_start|>assistant
pip install requests<|im_end|>"Trained on a MacBook Air. No rented A100s."
| Property | Value |
|---|---|
| Base model | Qwen/Qwen2.5-Coder-1.5B-Instruct |
| Fine-tune method | LoRA via MLX on Apple Silicon |
| LoRA rank / scale | 8 / 20.0 |
| Layers trained | 16 |
| Training iterations | 500 |
| Learning rate | 1e-5 |
| Training examples | 6,719 error/fix pairs across 15 categories |
| Export | Merged weights → Q4_K_M GGUF for Ollama |
| File | Description |
|---|---|
YOLO-Coder-1.5B-Q4_K_M.gguf | Q4_K_M quantized GGUF (~941MB) — use this with Ollama |
safetensors/ | fp16 safetensors — for further fine-tuning |
| YOLO-Coder-1.5B | YOLO-Coder-8B | |
|---|---|---|
| Size | ~941MB | ~4.4GB |
| RAM needed | ~2GB | ~6GB |
| Speed | <1s on Apple Silicon | 1–3s on Apple Silicon |
| Raw accuracy | 42.2% | 59.2% |
| Pipeline×3 accuracy | 71.1% | 77.1% |
| Best for | Speed, low-RAM machines | Hard errors, best accuracy |