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

Libraries

How to use krogoldAI/QueryRefiner-0.5B-v0.1-SFT 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-SFT")
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-SFT")
model = AutoModelForCausalLM.from_pretrained("krogoldAI/QueryRefiner-0.5B-v0.1-SFT", 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-SFT 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-SFT"
# 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-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

krogoldAI commited on Oct 12, 2025

Commit

650c607

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1 Parent(s): 310f17c

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README.md +18 -18

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@@ -18,7 +18,7 @@ base_model:
 QueryRefiner-0.5B-v0.1-SFT is a specialized query analysis and rephrasing model fine-tuned from [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) for Retrieval-Augmented Generation (RAG) systems. The model has been trained to analyze user queries through a structured, ontology-inspired reasoning framework that produces XML outputs with domain classification, intent detection, conceptual analysis, and optimized query reformulations designed to improve document retrieval precision.
-This model addresses a critical challenge in RAG systems: converting ambiguous, vague, or suboptimally-phrased user queries into retrieval-friendly formats through systematic decomposition and analysis. By employing a structured reasoning approach that explicitly identifies domains, intents, concepts, and relationships, the model produces higher-quality rephrasings informed by comprehensive query understanding. <!-- Rather than directly answering questions, the model performs query understanding and transformation, making it an ideal preprocessing component for information retrieval pipelines. -->
 **Note:** This model is currently in an experimental, beta stage. It represents an initial release focused on establishing baseline capabilities. Future iterations may address current limitations, expand domain coverage, or incorporate additional output features based on real-world deployment feedback.
@@ -331,17 +331,17 @@ print(analysis)
 ### Structural Validation
-To assess the model's ability to produce correctly formatted outputs, we evaluated structural conformance across 1,000 examples from the test split of [krogoldAI/rag-ambiguous-queries](https://huggingface.co/datasets/krogoldAI/rag-ambiguous-queries), comparing QueryRefiner-0.5B-v0.1 against its base model [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). The evaluation measured adherence to the required XML schema, including tag presence, well-formedness, element ordering, and confidence score formatting.
 #### Table 1 - Structural validity metrics (% of outputs meeting each requirement)
-|Metric|Qwen2.5-0.5B-Instruct|QueryRefiner-0.5B-v0.1|
-|----:|:-------------------:|:--------------------:|
-|Tag structure|10.8%|99.9%|
-|XML validity|40.8%|99.6%|
-|Order|2.0%|99.9%|
-|Confidence|3.1%|99.9%|
-|Perfectly structured output|0.0%|99.6%|
 Here, *tag structure* verifies that all required XML tags are present, *XML validity* ensures the output is well-formed and parseable, *order* confirms that required tags appear in the correct sequence, and *confidence* validates that confidence values are properly formatted and sum to `1.0`.
@@ -353,15 +353,15 @@ Beyond structural correctness, we evaluated the semantic quality of the model's
 #### Table 2 - Semantic scores
-|Metric|QueryRefiner-0.5B-v0.1|
-|----:|:--------------------:|
-|Domain accuracy|99.17 ± 7.17%|
-|Intent accuracy|98.39 ± 7.68%|
-|Ambiguity assessment|99.25 ± 5.20%|
-|Rephrasing quality|88.40 ± 17.79%|
-|Intent preservation|95.28 ± 15.17%|
-|Follows guidelines|97.21 ± 13.09%|
-|**Overall semantic score**|**96.28 ± 8.75%**|
 All values are reported as mean ± standard deviation (%), computed over test examples. The base model produced too few valid XML samples for meaningful semantic evaluation.

 QueryRefiner-0.5B-v0.1-SFT is a specialized query analysis and rephrasing model fine-tuned from [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) for Retrieval-Augmented Generation (RAG) systems. The model has been trained to analyze user queries through a structured, ontology-inspired reasoning framework that produces XML outputs with domain classification, intent detection, conceptual analysis, and optimized query reformulations designed to improve document retrieval precision.
+This model is part of a two-model series addressing a critical challenge in RAG systems: converting ambiguous, vague, or suboptimally-phrased user queries into retrieval-friendly formats through systematic decomposition and analysis. By employing a structured reasoning approach that explicitly identifies domains, intents, concepts, and relationships, the model produces higher-quality rephrasings informed by comprehensive query understanding. <!-- Rather than directly answering questions, the model performs query understanding and transformation, making it an ideal preprocessing component for information retrieval pipelines. -->
 **Note:** This model is currently in an experimental, beta stage. It represents an initial release focused on establishing baseline capabilities. Future iterations may address current limitations, expand domain coverage, or incorporate additional output features based on real-world deployment feedback.
 ### Structural Validation
+To assess the model's ability to produce correctly formatted outputs, we evaluated structural conformance across 1,000 examples from the test split of [krogoldAI/rag-ambiguous-queries](https://huggingface.co/datasets/krogoldAI/rag-ambiguous-queries), comparing both QueryRefiner-0.5B-v0.1 models against their base model [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). The evaluation measured adherence to the required XML schema, including tag presence, well-formedness, element ordering, and confidence score formatting.
 #### Table 1 - Structural validity metrics (% of outputs meeting each requirement)
+|Metric|Qwen2.5-0.5B-Instruct|QueryRefiner-0.5B-v0.1-SFT|QueryRefiner-0.5B-v0.1-GRPO|
+|----:|:-------------------:|:--------------------:|:--------------------:|
+|Tag structure|10.8%|99.9%|99.9%|
+|XML validity|41.0%|99.8%|99.9%|
+|Order|2.0%|99.9%|99.9%|
+|Confidence|3.1%|99.9%|99.9%|
+|Perfectly structured output|0.0%|99.8%|99.9%|
 Here, *tag structure* verifies that all required XML tags are present, *XML validity* ensures the output is well-formed and parseable, *order* confirms that required tags appear in the correct sequence, and *confidence* validates that confidence values are properly formatted and sum to `1.0`.
 #### Table 2 - Semantic scores
+|Metric|QueryRefiner-0.5B-v0.1-SFT|QueryRefiner-0.5B-v0.1-GRPO|
+|----:|:--------------------:|:--------------------:|
+|Domain accuracy|**98.97 ± 8.43%**|98.95 ± 8.46%|
+|Intent accuracy|98.20 ± 8.84%|**98.75 ± 8.05%**|
+|Ambiguity assessment|99.04 ± 6.84%|**99.40 ± 5.79%**|
+|Rephrasing quality|88.23 ± 18.20%|**90.33 ± 17.61%**|
+|Intent preservation|95.09 ± 15.74%|**96.02 ± 14.72%**|
+|Follows guidelines|97.07 ± 13.78%|**97.40 ± 12.20%**|
+|Overall semantic score|96.10 ± 9.74%|**96.81 ± 9.18%**|
 All values are reported as mean ± standard deviation (%), computed over test examples. The base model produced too few valid XML samples for meaningful semantic evaluation.