Instructions to use jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT")
model = AutoModelForCausalLM.from_pretrained("jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT")
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 jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaeyong2/Qwen2.5-3B-Instruct-Ja-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": "jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT

SGLang

How to use jaeyong2/Qwen2.5-3B-Instruct-Ja-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 "jaeyong2/Qwen2.5-3B-Instruct-Ja-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": "jaeyong2/Qwen2.5-3B-Instruct-Ja-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 "jaeyong2/Qwen2.5-3B-Instruct-Ja-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": "jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT with Docker Model Runner:
```
docker model run hf.co/jaeyong2/Qwen2.5-3B-Instruct-Ja-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model was trained on a Japanese dataset and built with Qwen.

Evaluation

llm-jp-eval script(colab)

!git clone https://github.com/llm-jp/llm-jp-eval.git
!cd llm-jp-eval && pip install -e .
!cd llm-jp-eval && python scripts/preprocess_dataset.py --dataset-name all --output-dir ./dataset_dir
!cd llm-jp-eval && python scripts/evaluate_llm.py -cn config.yaml model.pretrained_model_name_or_path=jaeyong2/Qwen2.5-0.5B-Instruct-JaMagpie-Preview tokenizer.pretrained_model_name_or_path=jaeyong2/Qwen2.5-0.5B-Instruct-JaMagpie-Preview dataset_dir=./dataset_dir/1.4.1/evaluation/test

llm-jp-eval	Qwen2.5-3B-Instruct	finetuning-model
AVG	0.4921	0.4895
CG	0.1000	0
EL	0.4770	0.4431
FA	0.1210	0.1246
HE	0.5550	0.5650
MC	0.7133	0.7900
MR	0.5400	0.6100
MT	0.6391	0.5982
NLI	0.6640	0.6640
QA	0.2638	0.3165
RC	0.8481	0.7837