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 6, 2025

Commit

60b76d7

verified ·

1 Parent(s): fff158e

Update README.md

Browse files

Files changed (1) hide show

README.md +31 -0

README.md CHANGED Viewed

@@ -20,6 +20,37 @@ This model addresses a critical challenge in RAG systems: converting ambiguous,
 **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.
 ## Intended Use
 The model is designed to serve as a query analysis layer in RAG systems, where it processes user inputs before they are sent to retrieval components. It can handle queries with varying degrees of ambiguity and identify when queries lack sufficient context for effective retrieval. The structured XML output enables downstream systems to make informed decisions about how to handle each query based on confidence scores, ambiguity flags, and the rephrased version.

 **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.
+## Examples
+<details>
+  <summary><i>Unroll to see Example #1</i></summary>
+User query:
+>How do I reset my password?
+Model output:
+```xml
+<query_analysis>
+  <domain ambiguous="false">
+    <candidate confidence="1.0">Information Technology</candidate>
+  </domain>
+  <intent ambiguous="false">
+    <candidate confidence="1.0">Troubleshooting, How-to Guidance</candidate>
+  </intent>
+  <concepts>
+    <explicit>password reset</explicit>
+    <implicit>password recovery, account security, IT services</implicit>
+  </concepts>
+  <insufficient_context>false</insufficient_context>
+  <rephrased>true</rephrased>
+  <rephrased_query>how to recover lost / forgotten [USER_PASSWORD] password</rephrased_query>
+</query_analysis>
+```
+</details>
 ## Intended Use
 The model is designed to serve as a query analysis layer in RAG systems, where it processes user inputs before they are sent to retrieval components. It can handle queries with varying degrees of ambiguity and identify when queries lack sufficient context for effective retrieval. The structured XML output enables downstream systems to make informed decisions about how to handle each query based on confidence scores, ambiguity flags, and the rephrased version.