Instructions to use krogoldAI/QueryRefiner-0.5B-v0.1-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use krogoldAI/QueryRefiner-0.5B-v0.1-GRPO with Transformers:

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

pipe = pipeline("text-generation", model="krogoldAI/QueryRefiner-0.5B-v0.1-GRPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("krogoldAI/QueryRefiner-0.5B-v0.1-GRPO")
model = AutoModelForCausalLM.from_pretrained("krogoldAI/QueryRefiner-0.5B-v0.1-GRPO", device_map="auto")
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 krogoldAI/QueryRefiner-0.5B-v0.1-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "krogoldAI/QueryRefiner-0.5B-v0.1-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "krogoldAI/QueryRefiner-0.5B-v0.1-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/krogoldAI/QueryRefiner-0.5B-v0.1-GRPO

SGLang

How to use krogoldAI/QueryRefiner-0.5B-v0.1-GRPO 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 "krogoldAI/QueryRefiner-0.5B-v0.1-GRPO" \
    --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": "krogoldAI/QueryRefiner-0.5B-v0.1-GRPO",
		"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 "krogoldAI/QueryRefiner-0.5B-v0.1-GRPO" \
        --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": "krogoldAI/QueryRefiner-0.5B-v0.1-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use krogoldAI/QueryRefiner-0.5B-v0.1-GRPO with Docker Model Runner:
```
docker model run hf.co/krogoldAI/QueryRefiner-0.5B-v0.1-GRPO
```

krogoldAI commited on Oct 12, 2025

Commit

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@@ -38,25 +38,25 @@ WARMUP_STEPS = 50
 |Metric|Qwen2.5-0.5B-Instruct|QueryRefiner-0.5B-v0.1-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT1-SFT2-v2|
 |----:|:-------------------:|:--------------------:|:--------------------:|:--------------------:|
 |Tag structure|10.8%|99.9%|100%|99.9%|
-|XML validity|40.8%|99.6%|99.8%|99.9%|
 |Order|2.0%|99.9%|100%|99.9%|
 |Confidence|3.1%|99.9%|100%|99.9%|
-|Perfectly structured output|0.0%|99.6%|99.8%|99.9%|
 #### Table 2 - Semantic scores
 |Metric|QueryRefiner-0.5B-v0.1-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT1-SFT2-v2|
 |----:|:--------------------:|:--------------------:|:--------------------:|
-|Domain accuracy|**99.17 ± 7.17%**|98.57 ± 10.25%|98.95 ± 8.46%|
-|Intent accuracy|98.39 ± 7.68%|**98.85 ± 8.30%**|98.75 ± 8.05%|
-|Ambiguity assessment|99.25 ± 5.20%|99.10 ± 6.66%|**99.40 ± 5.79%**|
-|Rephrasing quality|88.40 ± 17.79%|89.60 ± 17.50%|**90.33 ± 17.61%**|
-|Intent preservation|95.28 ± 15.17%|95.52 ± 15.00%|**96.02 ± 14.72%**|
-|Follows guidelines|97.21 ± 13.09%|97.07 ± 12.90%|**97.40 ± 12.20%**|
-|Overall semantic score|96.28 ± 8.75%|96.45 ± 9.52%|**96.81 ± 9.18%**|
-(Corrected for the GRPO-SFT models)
 ## Model Details

 |Metric|Qwen2.5-0.5B-Instruct|QueryRefiner-0.5B-v0.1-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT1-SFT2-v2|
 |----:|:-------------------:|:--------------------:|:--------------------:|:--------------------:|
 |Tag structure|10.8%|99.9%|100%|99.9%|
+|XML validity|40.8%|99.8%|99.8%|99.9%|
 |Order|2.0%|99.9%|100%|99.9%|
 |Confidence|3.1%|99.9%|100%|99.9%|
+|Perfectly structured output|0.0%|99.8%|99.8%|99.9%|
 #### Table 2 - Semantic scores
 |Metric|QueryRefiner-0.5B-v0.1-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT|QueryRefiner-0.5B-v0.1-GRPO-SFT1-SFT2-v2|
 |----:|:--------------------:|:--------------------:|:--------------------:|
+|Domain accuracy|98.97 ± 8.43%|98.57 ± 10.25%|**98.95 ± 8.46%**|
+|Intent accuracy|98.20 ± 8.84%|**98.85 ± 8.30%**|98.75 ± 8.05%|
+|Ambiguity assessment|99.04 ± 6.84%|99.10 ± 6.66%|**99.40 ± 5.79%**|
+|Rephrasing quality|88.23 ± 18.20%|89.60 ± 17.50%|**90.33 ± 17.61%**|
+|Intent preservation|95.09 ± 15.74%|95.52 ± 15.00%|**96.02 ± 14.72%**|
+|Follows guidelines|97.07 ± 13.78%|97.07 ± 12.90%|**97.40 ± 12.20%**|
+|Overall semantic score|96.10 ± 9.74%|96.45 ± 9.52%|**96.81 ± 9.18%**|
+(Corrected for the SFT + GRPO-SFT models)
 ## Model Details