Instructions to use alanwang2001/qwen3-4B-sentiment-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alanwang2001/qwen3-4B-sentiment-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/srv/scratch/z5526880/models/Qwen3-4B")
model = PeftModel.from_pretrained(base_model, "alanwang2001/qwen3-4B-sentiment-lora")

Transformers

How to use alanwang2001/qwen3-4B-sentiment-lora with Transformers:

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

pipe = pipeline("text-generation", model="alanwang2001/qwen3-4B-sentiment-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alanwang2001/qwen3-4B-sentiment-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alanwang2001/qwen3-4B-sentiment-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alanwang2001/qwen3-4B-sentiment-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alanwang2001/qwen3-4B-sentiment-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alanwang2001/qwen3-4B-sentiment-lora

SGLang

How to use alanwang2001/qwen3-4B-sentiment-lora 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 "alanwang2001/qwen3-4B-sentiment-lora" \
    --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": "alanwang2001/qwen3-4B-sentiment-lora",
		"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 "alanwang2001/qwen3-4B-sentiment-lora" \
        --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": "alanwang2001/qwen3-4B-sentiment-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alanwang2001/qwen3-4B-sentiment-lora with Docker Model Runner:
```
docker model run hf.co/alanwang2001/qwen3-4B-sentiment-lora
```

qwen3-4B-sentiment-lora

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

Loss: 0.5539

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: 0.0001
train_batch_size: 64
eval_batch_size: 64
seed: 42
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: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.7054	0.2075	50	0.6754
0.6115	0.4149	100	0.5981
0.5950	0.6224	150	0.5835
0.5743	0.8299	200	0.5668
0.5065	1.0373	250	0.5620
0.5057	1.2448	300	0.5533
0.4914	1.4523	350	0.5517
0.4953	1.6598	400	0.5480
0.4884	1.8672	450	0.5413
0.4028	2.0747	500	0.5595
0.4098	2.2822	550	0.5577
0.3910	2.4896	600	0.5554
0.4035	2.6971	650	0.5556
0.4106	2.9046	700	0.5541

Framework versions

PEFT 0.18.1
Transformers 5.2.0
Pytorch 2.11.0+cu130
Datasets 4.0.0
Tokenizers 0.22.2

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