Instructions to use circle1018/qwen3.5-0.8b-science-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use circle1018/qwen3.5-0.8b-science-sft with Transformers:

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

pipe = pipeline("text-generation", model="circle1018/qwen3.5-0.8b-science-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("circle1018/qwen3.5-0.8b-science-sft")
model = AutoModelForCausalLM.from_pretrained("circle1018/qwen3.5-0.8b-science-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 circle1018/qwen3.5-0.8b-science-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "circle1018/qwen3.5-0.8b-science-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": "circle1018/qwen3.5-0.8b-science-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/circle1018/qwen3.5-0.8b-science-sft

SGLang

How to use circle1018/qwen3.5-0.8b-science-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 "circle1018/qwen3.5-0.8b-science-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": "circle1018/qwen3.5-0.8b-science-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 "circle1018/qwen3.5-0.8b-science-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": "circle1018/qwen3.5-0.8b-science-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use circle1018/qwen3.5-0.8b-science-sft with Docker Model Runner:
```
docker model run hf.co/circle1018/qwen3.5-0.8b-science-sft
```

qwen3.5-0.8b-science-sft

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: 0.7586

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: 1.5e-05
train_batch_size: 2
eval_batch_size: 8
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: 30
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.5992	0.2725	50	1.5661
1.1612	0.5450	100	1.2404
1.0464	0.8174	150	1.0641
0.6144	1.0872	200	0.9381
0.6478	1.3597	250	0.8737
0.5559	1.6322	300	0.8101
0.5626	1.9046	350	0.7701
0.3460	2.1744	400	0.7633
0.3695	2.4469	450	0.7587
0.3762	2.7193	500	0.7586
0.3643	2.9918	550	0.7586
0.3643	3.0	552	0.7586

Framework versions

Transformers 5.15.0
Pytorch 2.13.0+xpu
Datasets 5.0.1
Tokenizers 0.22.2

Downloads last month: 269

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for circle1018/qwen3.5-0.8b-science-sft

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Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

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