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

5ffee93

verified ·

1 Parent(s): a35d52d

Update README.md

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README.md +33 -32

README.md CHANGED Viewed

@@ -62,6 +62,39 @@ Model output:
 User query:
 > How do they enhance its performance?
 Model output:
@@ -107,38 +140,6 @@ Model output:
 </details>
-<details>
-  <summary><i>Unroll to see Example #3</i></summary>
-User query:
->Which legal aspects of data gathering must be considered before training a language model?
-Model output:
-```xml
-<query_analysis>
-  <domain ambiguous="false">
-    <candidate confidence="1.0">Legal</candidate>
-  </domain>
-  <intent ambiguous="false">
-    <candidate confidence="1.0">Data privacy and ethics</candidate>
-  </intent>
-  <concepts>
-    <explicit>data gathering, language models</explicit>
-    <implicit>legal considerations, ethical practices</implicit>
-  </concepts>
-  <relations>
-    <relation subject="data gathering" predicate="must consider" object="legal aspects"/>
-    <relation subject="language models" predicate="require" object="legal considerations"/>
-  </relations>
-  <insufficient_context>false</insufficient_context>
-  <rephrased>true</rephrased>
-  <rephrased_query>legal requirements for data collection and usage when developing natural language processing models</rephrased_query>
-</query_analysis>
-```
-</details>
 ## Intended Use

 User query:
+>Which legal aspects of data gathering must be considered before training a language model?
+Model output:
+```xml
+<query_analysis>
+  <domain ambiguous="false">
+    <candidate confidence="1.0">Legal</candidate>
+  </domain>
+  <intent ambiguous="false">
+    <candidate confidence="1.0">Data privacy and ethics</candidate>
+  </intent>
+  <concepts>
+    <explicit>data gathering, language models</explicit>
+    <implicit>legal considerations, ethical practices</implicit>
+  </concepts>
+  <relations>
+    <relation subject="data gathering" predicate="must consider" object="legal aspects"/>
+    <relation subject="language models" predicate="require" object="legal considerations"/>
+  </relations>
+  <insufficient_context>false</insufficient_context>
+  <rephrased>true</rephrased>
+  <rephrased_query>legal requirements for data collection and usage when developing natural language processing models</rephrased_query>
+</query_analysis>
+```
+</details>
+<details>
+  <summary><i>Unroll to see Example #3</i></summary>
+User query:
 > How do they enhance its performance?
 Model output:
 </details>
 ## Intended Use