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 13, 2025

Commit

7bf5eac

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@@ -175,7 +175,7 @@ The training code and the reward function code can be found [here](Codes/01%20-%
 Following structural learning through GRPO, the model underwent supervised fine-tuning on the complete [krogoldAI/rag-query-analysis](https://huggingface.co/datasets/krogoldAI/rag-query-analysis) dataset (7,305 examples) for 2 epochs with a learning rate of 2e-5. This phase captured broad semantic patterns across diverse query types and domains.
-The training code can be found <ins>here</ins>.
 ### Phase 3: Depth-Focused Supervised Fine-Tuning
@@ -183,7 +183,7 @@ The final phase focused on precision refinement using only the highest-quality 3
 This progressive training approach ensures the model achieves both reliable formatting and nuanced semantic understanding. All training was performed on an NVIDIA A100 SXM GPU.
-The training code can be found <ins>here</ins>.
 ## Model Capabilities

 Following structural learning through GRPO, the model underwent supervised fine-tuning on the complete [krogoldAI/rag-query-analysis](https://huggingface.co/datasets/krogoldAI/rag-query-analysis) dataset (7,305 examples) for 2 epochs with a learning rate of 2e-5. This phase captured broad semantic patterns across diverse query types and domains.
+The training code can be found [here](Codes/02%20-%20Breadth-focused%20full%20SFT.py).
 ### Phase 3: Depth-Focused Supervised Fine-Tuning
 This progressive training approach ensures the model achieves both reliable formatting and nuanced semantic understanding. All training was performed on an NVIDIA A100 SXM GPU.
+The training code can be found [here](Codes/03%20-%20Depth-focused%20full%20SFT.py).
 ## Model Capabilities