Instructions to use Bagsangbin/Qwen3-8B-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bagsangbin/Qwen3-8B-QA with Transformers:

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

pipe = pipeline("text-generation", model="Bagsangbin/Qwen3-8B-QA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bagsangbin/Qwen3-8B-QA")
model = AutoModelForCausalLM.from_pretrained("Bagsangbin/Qwen3-8B-QA")
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

vLLM

How to use Bagsangbin/Qwen3-8B-QA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bagsangbin/Qwen3-8B-QA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bagsangbin/Qwen3-8B-QA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bagsangbin/Qwen3-8B-QA

SGLang

How to use Bagsangbin/Qwen3-8B-QA 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 "Bagsangbin/Qwen3-8B-QA" \
    --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": "Bagsangbin/Qwen3-8B-QA",
		"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 "Bagsangbin/Qwen3-8B-QA" \
        --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": "Bagsangbin/Qwen3-8B-QA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bagsangbin/Qwen3-8B-QA with Docker Model Runner:
```
docker model run hf.co/Bagsangbin/Qwen3-8B-QA
```

Qwen3-8B-QA

This model is a fine-tuned version of Qwen/Qwen3-8B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.8702
Accuracy: 0.0007

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: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	39	0.8670	0.0007
1.2237	2.0	78	0.6869	0.0008
0.4504	3.0	117	0.7132	0.0008
0.1753	4.0	156	0.7544	0.0008
0.1753	5.0	195	0.7910	0.0007
0.0849	6.0	234	0.8127	0.0007
0.0583	7.0	273	0.8425	0.0007
0.0489	8.0	312	0.8606	0.0007
0.0451	9.0	351	0.8681	0.0007
0.0451	10.0	390	0.8702	0.0007

Framework versions

Transformers 4.53.2
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.2

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

F16

Model tree for Bagsangbin/Qwen3-8B-QA

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(1590)

this model