qwen3-0.6b-promql/README.md

license, base_model, library_name, tags, pipeline_tag

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

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)

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.