Instructions to use circle1018/qwen3-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use circle1018/qwen3-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="circle1018/qwen3-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("circle1018/qwen3-finetuned")
model = AutoModelForCausalLM.from_pretrained("circle1018/qwen3-finetuned", 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 circle1018/qwen3-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "circle1018/qwen3-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "circle1018/qwen3-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/circle1018/qwen3-finetuned

SGLang

How to use circle1018/qwen3-finetuned 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 "circle1018/qwen3-finetuned" \
    --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": "circle1018/qwen3-finetuned",
		"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 "circle1018/qwen3-finetuned" \
        --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": "circle1018/qwen3-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use circle1018/qwen3-finetuned with Docker Model Runner:
```
docker model run hf.co/circle1018/qwen3-finetuned
```

qwen3-finetuned

This model is a fine-tuned version of Qwen/Qwen3.5-0.8B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.7912

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: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
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: 20
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.0582	0.2625	25	2.0773
2.0197	0.5249	50	1.9484
1.8049	0.7874	75	1.8555
1.7941	1.0420	100	1.8087
1.4336	1.3045	125	1.8126
1.4069	1.5669	150	1.7940
1.4073	1.8294	175	1.7844
1.2006	2.0840	200	1.7792
1.2856	2.3465	225	1.7891
1.2283	2.6089	250	1.7919
1.2139	2.8714	275	1.7907
1.2408	3.0	288	1.7912

Framework versions

Transformers 5.15.0
Pytorch 2.13.0+xpu
Datasets 5.0.1
Tokenizers 0.22.2

Downloads last month: 855

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for circle1018/qwen3-finetuned

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

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