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

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@@ -1,6 +1,11 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Rephraser-0.5B-v0.1-SFT
@@ -30,7 +35,7 @@ The training data underwent rigorous quality assurance through a dual evaluation
 ## Training Procedure
-The model underwent full fine-tuning (not parameter-efficient methods like LoRA) of all parameters in [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). Training was conducted over three epochs with a per-device batch size of \\( 4 \\) and gradient accumulation over \\( 4 \\) steps, yielding an effective batch size of \\( 16 \\). The learning rate was set to \\( 2\times 10^{-5} \\) with a weight decay of \\( 0.01 \\) to prevent overfitting. A warmup period of \\( 100 \\) steps allowed the model to gradually adapt to the task-specific distribution before reaching the full learning rate. Training was performed on an NVIDIA A100 SXM GPU.
 This training configuration was chosen to balance thorough adaptation to the structured output task while preserving the base model's language understanding capabilities. The full fine-tuning approach, rather than parameter-efficient alternatives, ensures the model fully internalizes the strict XML formatting requirements and the nuanced query analysis patterns present in the training data.

 ---
 library_name: transformers
+datasets:
+- krogoldAI/Query_rephraser_SFT
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
 ---
 # Rephraser-0.5B-v0.1-SFT
 ## Training Procedure
+The model underwent full fine-tuning (not parameter-efficient methods like LoRA) of all parameters in [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). Training was conducted over three epochs with a per-device batch size of 4 and gradient accumulation over 4 steps, yielding an effective batch size of 16. The learning rate was set to 2e-5 with a weight decay of 0.01 to prevent overfitting. A warmup period of 100 steps allowed the model to gradually adapt to the task-specific distribution before reaching the full learning rate. Training was performed on an NVIDIA A100 SXM GPU.
 This training configuration was chosen to balance thorough adaptation to the structured output task while preserving the base model's language understanding capabilities. The full fine-tuning approach, rather than parameter-efficient alternatives, ensures the model fully internalizes the strict XML formatting requirements and the nuanced query analysis patterns present in the training data.