Instructions to use Atikarahmanda/Qwen3-4B-SFT-Multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Atikarahmanda/Qwen3-4B-SFT-Multitask with Transformers:

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

pipe = pipeline("text-generation", model="Atikarahmanda/Qwen3-4B-SFT-Multitask")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Atikarahmanda/Qwen3-4B-SFT-Multitask")
model = AutoModelForCausalLM.from_pretrained("Atikarahmanda/Qwen3-4B-SFT-Multitask")
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 Atikarahmanda/Qwen3-4B-SFT-Multitask with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Atikarahmanda/Qwen3-4B-SFT-Multitask"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atikarahmanda/Qwen3-4B-SFT-Multitask",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Atikarahmanda/Qwen3-4B-SFT-Multitask

SGLang

How to use Atikarahmanda/Qwen3-4B-SFT-Multitask 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 "Atikarahmanda/Qwen3-4B-SFT-Multitask" \
    --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": "Atikarahmanda/Qwen3-4B-SFT-Multitask",
		"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 "Atikarahmanda/Qwen3-4B-SFT-Multitask" \
        --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": "Atikarahmanda/Qwen3-4B-SFT-Multitask",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Atikarahmanda/Qwen3-4B-SFT-Multitask with Docker Model Runner:
```
docker model run hf.co/Atikarahmanda/Qwen3-4B-SFT-Multitask
```

Qwen3-4B SFT — Multitask Bahasa Indonesia

Model hasil fine-tuning (LoRA, sudah di-merge ke base weights) dari aitf-kpm-ugm/Qwen3-4B-CPT-Base untuk berbagai tugas NLP Bahasa Indonesia.

Detail Model

Atribut	Nilai
Base model	`aitf-kpm-ugm/Qwen3-4B-CPT-Base`
Metode fine-tune	LoRA (r=64, alpha=128)
Status adapter	Merged ke base weights
Bahasa output	Bahasa Indonesia
Format chat	Alpaca
Precision	bfloat16
Max seq length	2048
Training epochs	3
Best checkpoint	step 2400
Best val loss	0.3566

Task yang Didukung

Sentimen Analysis — klasifikasi positif / netral / negatif
Justifier — klasifikasi is_relevant: true / false
Kategorisasi Issue — klasifikasi sub_category_label

Cara Pakai

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

REPO = "Atikarahmanda/Qwen3-4B-SFT-Multitask"

tokenizer = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForCausalLM.from_pretrained(
    REPO,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()

messages = [
    {"role": "system", "content": "Sistem prompt sesuai task."},
    {"role": "user",   "content": "Input artikel di sini."},
]

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
input_len = inputs.input_ids.shape[1]

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
    )

print(tokenizer.decode(out[0, input_len:], skip_special_tokens=True))
```

---

## Training Details

- **Framework**: Unsloth + TRL SFTTrainer
- **LoRA config**: r=64, alpha=128
- **Optimizer**: AdamW 8-bit
- **LR scheduler**: Cosine, warmup ratio 0.03
- **Effective batch size**: 6 x 8 = 48
- **train_on_responses_only**: Ya

---

## Lisensi

Mengikuti lisensi base model: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Atikarahmanda/Qwen3-4B-SFT-Multitask

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Qwen/Qwen3-4B-Base

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aitf-kpm-ugm/Qwen3-4B-CPT-Base

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