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

460006b

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@@ -45,6 +45,34 @@ The model generates structured XML analyses following a specific schema that inc
 The rephrasing capability focuses on retrieval optimization rather than query answering. The model transforms queries by using specific terminology likely to appear in relevant documents, expanding acronyms when contextually appropriate, adding disambiguating context, and making implicit references explicit through placeholder notation such as `[PERSON]` or `[COMPANY]`. Importantly, the model has learned to preserve already-optimal queries unchanged, recognizing when rephrasing would not improve retrieval effectiveness.
 ## 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 rephrasing capability focuses on retrieval optimization rather than query answering. The model transforms queries by using specific terminology likely to appear in relevant documents, expanding acronyms when contextually appropriate, adding disambiguating context, and making implicit references explicit through placeholder notation such as `[PERSON]` or `[COMPANY]`. Importantly, the model has learned to preserve already-optimal queries unchanged, recognizing when rephrasing would not improve retrieval effectiveness.
+The default output schema is the following:
+```xml
+<query_analysis>
+  <domain ambiguous="true|false">
+    <candidate confidence="X.X">...</candidate>
+  </domain>
+  <intent ambiguous="true|false">
+    <candidate confidence="X.X">...</candidate>
+  </intent>
+  <!-- Optional sections -->
+  <concepts>
+    <explicit>...</explicit>
+    <implicit>...</implicit>
+  </concepts>
+  <relations>
+    <relation subject="..." predicate="..." object="..."/>
+  </relations>
+  <normalized_terms>
+    <term original="..." normalized="..."/>
+  </normalized_terms>
+  <!-- End optional sections -->
+  <insufficient_context>true|false</insufficient_context>
+  <rephrased>true|false</rephrased>
+  <rephrased_query>...</rephrased_query>
+</query_analysis>
+```
 ## 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.