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 5, 2025

Commit

304cfd0

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Update README.md

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@@ -8,13 +8,13 @@ base_model:
 - Qwen/Qwen2.5-0.5B-Instruct
 ---
-# Rephraser-0.5B-v0.1-SFT
 <!-- RAG Query Analyzer 0.5B v0.1 -->
 ## Model Description
-Rephraser-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. -->
@@ -81,7 +81,7 @@ The default output schema is the following:
 ## Limitations and Considerations
-As a 0.5B parameter model, Rephraser-0.5B-v0.1-SFT prioritizes efficiency and deployability over the capabilities of larger language models. While it performs well on the types of queries represented in its training distribution, performance may degrade on highly specialized domains, multilingual queries, or query types significantly different from the training examples. The model focuses exclusively on English-language queries and has been optimized for the specific XML output format defined in its training.
 The model's ambiguity detection and confidence scoring reflect patterns learned from the training data, which includes both natural and synthetically augmented ambiguous queries. While the training process incorporated diverse ambiguity levels, edge cases or novel forms of ambiguity may not be handled with the same reliability as more common patterns. Users should consider the model's confidence scores as informative signals rather than calibrated probabilities.

 - Qwen/Qwen2.5-0.5B-Instruct
 ---
+# Query-Refiner-0.5B-v0.1-SFT
 <!-- RAG Query Analyzer 0.5B v0.1 -->
 ## Model Description
+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. -->
 ## Limitations and Considerations
+As a 0.5B parameter model, QueryRefiner-0.5B-v0.1-SFT prioritizes efficiency and deployability over the capabilities of larger language models. While it performs well on the types of queries represented in its training distribution, performance may degrade on highly specialized domains, multilingual queries, or query types significantly different from the training examples. The model focuses exclusively on English-language queries and has been optimized for the specific XML output format defined in its training.
 The model's ambiguity detection and confidence scoring reflect patterns learned from the training data, which includes both natural and synthetically augmented ambiguous queries. While the training process incorporated diverse ambiguity levels, edge cases or novel forms of ambiguity may not be handled with the same reliability as more common patterns. Users should consider the model's confidence scores as informative signals rather than calibrated probabilities.