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Model: lazos/qwen3-0.6b-promql
Source: Original Platform

README.md

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+---
+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.