qwen3.5_finetuned

Qwen3.5-0.8B — OS System Command Intent Parser

Fine-tuned from Qwen/Qwen3.5-0.8B using QLoRA on 1,059 system monitoring examples. Given a natural language system alert, the model outputs a structured JSON action plan describing intent, steps, risk level, and confidence.

---

Example

Input: RAM at 95%, system becoming unresponsive

Output:

{
  "intent": "memory_check",
  "steps": [
    { "action": "check_memory", "params": {} },
    { "action": "list_processes", "params": { "sort": "memory", "limit": 10 } }
  ],
  "risk": "high",
  "confidence": 0.92
}

---

Supported Intents

---

Output Schema

---

Training Details

---

Prompt Format

Uses Qwen3.5's native ChatML format:

<|im_start|>system
You are a structured data extractor. Given an input, output a valid JSON object and nothing else.<|im_end|>
<|im_start|>user
{system alert}<|im_end|>
<|im_start|>assistant

---

GGUF — Local Inference

# Download and run (auto-downloads on first run)
llama-server -hf exoro/qwen3.5:Q4_K_M --reasoning-budget 0 --port 8080

# Test
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5",
    "messages": [
      {"role": "system", "content": "You are a structured data extractor. Given an input, output a valid JSON object and nothing else."},
      {"role": "user", "content": "RAM at 95%, system becoming unresponsive"}
    ]
  }'

---

Limitations

• Trained on system-monitoring domain — may not generalise to unrelated tasks
• Risk and confidence values are heuristic, not calibrated
• Small model (0.8B) — complex multi-step plans may occasionally be incomplete

---