Instructions to use wangwang0204/adl-hw2-qwen3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wangwang0204/adl-hw2-qwen3 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base_model, "wangwang0204/adl-hw2-qwen3")

Transformers

How to use wangwang0204/adl-hw2-qwen3 with Transformers:

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

pipe = pipeline("text-generation", model="wangwang0204/adl-hw2-qwen3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wangwang0204/adl-hw2-qwen3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wangwang0204/adl-hw2-qwen3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wangwang0204/adl-hw2-qwen3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wangwang0204/adl-hw2-qwen3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wangwang0204/adl-hw2-qwen3

SGLang

How to use wangwang0204/adl-hw2-qwen3 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 "wangwang0204/adl-hw2-qwen3" \
    --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": "wangwang0204/adl-hw2-qwen3",
		"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 "wangwang0204/adl-hw2-qwen3" \
        --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": "wangwang0204/adl-hw2-qwen3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wangwang0204/adl-hw2-qwen3 with Docker Model Runner:
```
docker model run hf.co/wangwang0204/adl-hw2-qwen3
```

adl-hw2-qwen3

This model is a fine-tuned version of Qwen/Qwen3-4B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0910

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 30
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
15.2427	0.128	10	0.3634
0.18	0.256	20	0.1202
0.1177	0.384	30	0.1100
0.1055	0.512	40	0.1040
0.1045	0.64	50	0.0994
0.0964	0.768	60	0.0965
0.0977	0.896	70	0.0948
0.0925	1.0128	80	0.0936
0.0914	1.1408	90	0.0930
0.0882	1.2688	100	0.0920
0.0868	1.3968	110	0.0919
0.0899	1.5248	120	0.0915
0.0862	1.6528	130	0.0914
0.0894	1.7808	140	0.0909
0.0891	1.9088	150	0.0910

Framework versions

PEFT 0.17.1
Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

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Model tree for wangwang0204/adl-hw2-qwen3

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