Instructions to use zorxor/qwen3.5-0.8B-finetuned-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zorxor/qwen3.5-0.8B-finetuned-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zorxor/qwen3.5-0.8B-finetuned-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zorxor/qwen3.5-0.8B-finetuned-merged")
model = AutoModelForCausalLM.from_pretrained("zorxor/qwen3.5-0.8B-finetuned-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zorxor/qwen3.5-0.8B-finetuned-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zorxor/qwen3.5-0.8B-finetuned-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zorxor/qwen3.5-0.8B-finetuned-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zorxor/qwen3.5-0.8B-finetuned-merged

SGLang

How to use zorxor/qwen3.5-0.8B-finetuned-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zorxor/qwen3.5-0.8B-finetuned-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zorxor/qwen3.5-0.8B-finetuned-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zorxor/qwen3.5-0.8B-finetuned-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zorxor/qwen3.5-0.8B-finetuned-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zorxor/qwen3.5-0.8B-finetuned-merged with Docker Model Runner:
```
docker model run hf.co/zorxor/qwen3.5-0.8B-finetuned-merged
```

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

Intent	Description
`memory_check`	RAM pressure, OOM, swap issues
`cpu_check`	High CPU, thermal throttling, load average
`cleanup_disk`	Disk full, large files, temp cleanup
`disk_check`	SMART errors, RAID, filesystem corruption
`network_check`	Interface down, DNS, latency, VPN
`service_check`	Service crashed, restart needed
`process_check`	Zombie, runaway, file descriptor limits
`security_alert`	Brute force, intrusion, exploit detected
`system_check`	Kernel panic, hardware errors, reboot
`diagnose`	Multi-condition or ambiguous alerts
`reject`	Non-system inputs, dangerous commands

Output Schema

Field	Type	Description
`intent`	string	Alert category
`steps`	array	Ordered actions with params
`risk`	string	`low` / `medium` / `high`
`confidence`	float	Model confidence 0–1

Training Details

Property	Value
Base model	Qwen/Qwen3.5-0.8B
Method	QLoRA (LoRA r=16, α=16)
Dataset	1,059 custom JSONL examples
Epochs	3
Optimizer	adamw_8bit
Learning rate	2e-4
LR scheduler	cosine
Max sequence length	1024
Hardware	Tesla T4 — Google Colab

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"}
    ]
  }'

File	Size	Use case
`Qwen3.5-0.8B.Q4_K_M.gguf`	~505 MB	Best balance
`Qwen3.5-0.8B.Q8_0.gguf`	~774 MB	Higher quality
`Qwen3.5-0.8B.F16.gguf`	~1.6 GB	Full precision

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

Related Repos

Repo	Contents
exoro/qwen3.5	This repo — merged model + GGUF
exoro/qwen3.5-0.8B-finetuned-merged	Earlier checkpoint

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