Instructions to use antechit03/qwen3-viet-multi-task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use antechit03/qwen3-viet-multi-task with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "antechit03/qwen3-viet-multi-task")

Transformers

How to use antechit03/qwen3-viet-multi-task with Transformers:

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

pipe = pipeline("text-generation", model="antechit03/qwen3-viet-multi-task")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("antechit03/qwen3-viet-multi-task", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use antechit03/qwen3-viet-multi-task with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "antechit03/qwen3-viet-multi-task"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "antechit03/qwen3-viet-multi-task",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/antechit03/qwen3-viet-multi-task

SGLang

How to use antechit03/qwen3-viet-multi-task 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 "antechit03/qwen3-viet-multi-task" \
    --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": "antechit03/qwen3-viet-multi-task",
		"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 "antechit03/qwen3-viet-multi-task" \
        --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": "antechit03/qwen3-viet-multi-task",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use antechit03/qwen3-viet-multi-task with Docker Model Runner:
```
docker model run hf.co/antechit03/qwen3-viet-multi-task
```

qwen3-viet-multi-task / README.md

antechit03

qwen3-viet-multi-task

b183834 verified 7 months ago

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metadata

library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
  - base_model:adapter:Qwen/Qwen3-0.6B
  - lora
  - transformers
pipeline_tag: text-generation
model-index:
  - name: qwen3-viet-multi-task
    results: []

qwen3-viet-multi-task

This model is a fine-tuned version of Qwen/Qwen3-0.6B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8918

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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: linear
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
1.8436	1.0	219	0.9183
0.8737	2.0	438	0.9007
0.8623	3.0	657	0.8938
0.8576	4.0	876	0.8918

Framework versions

PEFT 0.18.0
Transformers 4.57.1
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1