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

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@@ -142,7 +142,7 @@ Approximately 20% of the training examples include queries with systematically i
 The training data underwent rigorous quality assurance through a dual evaluation framework. Each example was validated for strict XML schema conformance and semantically evaluated using an LLM-as-a-judge protocol with six quality dimensions. Only examples achieving both perfect structural validity and high semantic quality scores were included in the final dataset, ensuring the model was trained exclusively on gold-standard examples.
-Additionally, the model was trained on 3,000 randomly chosen examples of [krogoldAI/rag-ambiguous-queries](https://huggingface.co/datasets/krogoldAI/rag-ambiguous-queries), a dataset of queries derived designed specifically for the GRPO phase. This dataset includes queries distributed across four ambiguity levels:
 - no ambiguity (45%, original well-formed queries),
 - low ambiguity (30%, minor vagueness from removing one specific detail),
 - medium ambiguity (20%, moderate ambiguity with multiple missing references),

 The training data underwent rigorous quality assurance through a dual evaluation framework. Each example was validated for strict XML schema conformance and semantically evaluated using an LLM-as-a-judge protocol with six quality dimensions. Only examples achieving both perfect structural validity and high semantic quality scores were included in the final dataset, ensuring the model was trained exclusively on gold-standard examples.
+Additionally, the model was trained on 3,000 randomly chosen examples of [krogoldAI/rag-ambiguous-queries](https://huggingface.co/datasets/krogoldAI/rag-ambiguous-queries), a dataset of queries derived from [glaiveai/RAG-v1](https://huggingface.co/datasets/glaiveai/RAG-v1) designed specifically for the GRPO phase. This dataset includes queries distributed across four synthetically induced ambiguity levels:
 - no ambiguity (45%, original well-formed queries),
 - low ambiguity (30%, minor vagueness from removing one specific detail),
 - medium ambiguity (20%, moderate ambiguity with multiple missing references),