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 14, 2025

Commit

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@@ -463,4 +463,18 @@ Performance characteristics will vary based on query type, domain, and ambiguity
 ## Acknowledgments
-This model builds upon [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) by the Qwen team at Alibaba Cloud. The training data incorporates queries from [rag-datasets/rag-mini-wikipedia](https://huggingface.co/datasets/rag-datasets/rag-mini-wikipedia), [razbit96/Ambiguity-Handling-in-User-Queries](https://huggingface.co/datasets/razbit96/Ambiguity-Handling-in-User-Queries), and [glaiveai/RAG-v1](https://huggingface.co/datasets/glaiveai/RAG-v1).

 ## Acknowledgments
+This model builds upon [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) by the Qwen team at Alibaba Cloud. The training data incorporates queries from [rag-datasets/rag-mini-wikipedia](https://huggingface.co/datasets/rag-datasets/rag-mini-wikipedia), [razbit96/Ambiguity-Handling-in-User-Queries](https://huggingface.co/datasets/razbit96/Ambiguity-Handling-in-User-Queries), and [glaiveai/RAG-v1](https://huggingface.co/datasets/glaiveai/RAG-v1).
+---
+#### A. Per-ambiguity breakdown + sample sizes
+|                      | NONE           | LOW            | MEDIUM         | HIGH           |
+|:---------------------|:---------------|:---------------|:---------------|:---------------|
+| domain_accuracy      | 98.82 ± 9.48%  | 98.91 ± 8.53%  | 99.26 ± 6.54%  | 99.04 ± 4.85%  |
+| intent_accuracy      | 98.77 ± 8.94%  | 99.24 ± 6.93%  | 98.77 ± 6.46%  | 95.67 ± 10.76% |
+| ambiguity_assessment | 99.27 ± 6.56%  | 99.33 ± 6.46%  | 99.63 ± 3.02%  | 100.00 ± 0.00% |
+| rephrasing_quality   | 90.81 ± 17.49% | 89.98 ± 18.80% | 90.52 ± 15.11% | 87.50 ± 20.71% |
+| intent_preservation  | 96.13 ± 14.72% | 95.88 ± 15.71% | 96.80 ± 12.02% | 92.79 ± 18.08% |
+| follows_guidelines   | 97.14 ± 13.26% | 97.05 ± 12.91% | 98.89 ± 7.17%  | 95.67 ± 13.76% |
+| semantic_score       | 96.82 ± 9.58%  | 96.73 ± 9.80%  | 97.31 ± 6.82%  | 95.11 ± 10.06% |