---
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
tags:
- lora
- qlora
- fine-tuned
- text-generation
pipeline_tag: text-generation
---

# qwen3-0.6b-promql

A small language model fine-tuned with QLoRA SFT on top of `Qwen/Qwen3-0.6B-Base` for an instruction -> rewrite task.

Training code, data, and benchmarks: [https://github.com/lajosbencz/lfm-train](https://github.com/lajosbencz/lfm-train)

## Prompt format

- System prompt: `You are a PromQL optimization expert. Given a PromQL expression and an instruction, rewrite the expression according to the instruction. Output only the improved PromQL expression, no explanation.`
- Input label: `Query`

## Usage (transformers)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "lazos/qwen3-0.6b-promql"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "system", "content": "You are a PromQL optimization expert. Given a PromQL expression and an instruction, rewrite the expression according to the instruction. Output only the improved PromQL expression, no explanation."},
    {"role": "user", "content": "<your instruction>\n\nQuery:\n<your input>"},
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
```

A Q4_K_M GGUF quantization is included for use with llama.cpp / Ollama.