45 lines
1.6 KiB
Markdown
45 lines
1.6 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen3-0.6B-Base
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library_name: transformers
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tags:
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- lora
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- qlora
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- fine-tuned
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- text-generation
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pipeline_tag: text-generation
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---
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# qwen3-0.6b-promql
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A small language model fine-tuned with QLoRA SFT on top of `Qwen/Qwen3-0.6B-Base` for an instruction -> rewrite task.
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Training code, data, and benchmarks: [https://github.com/lajosbencz/lfm-train](https://github.com/lajosbencz/lfm-train)
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## Prompt format
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- 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.`
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- Input label: `Query`
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## Usage (transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "lazos/qwen3-0.6b-promql"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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messages = [
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{"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."},
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{"role": "user", "content": "<your instruction>\n\nQuery:\n<your input>"},
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]
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inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt"
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).to(model.device)
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out = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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A Q4_K_M GGUF quantization is included for use with llama.cpp / Ollama.
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