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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Adicandra/Qwen3-4B-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": "Adicandra/Qwen3-4B-Multitask",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Qwen3-4B-Multitask / README.md

Adicandra

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1b71505 verified about 1 month ago

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metadata

base_model: aitf-kpm-ugm/Qwen3-4B-CPT-Base
library_name: transformers
language:
  - id
license: apache-2.0
pipeline_tag: text-generation
tags:
  - qwen3
  - bahasa-indonesia
  - lora
  - lora-merged
  - sft
  - multitask
  - sentiment-analysis
  - summarization
  - chatml
datasets:
  - custom

Qwen3-4B SFT-CPT — Multitask Bahasa Indonesia (LoRA merged)

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

Deskripsi Singkat

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	ChatML (Qwen3-instruct)
EOS token	`<\|im_end\|>`
Precision	bfloat16
Max seq length	2048
Training epochs	3
Train samples	~93,579
Best val loss	0.343

Cara Pakai (Inference)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

REPO = "Adicandra/Qwen3-4B-Multitask"

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

messages = [
    {"role": "system", "content": "Kamu adalah asisten AI yang membantu."},
    {"role": "user",   "content": "Ringkaskan teks berikut: ..."},
]

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]

im_end_id = tokenizer.convert_tokens_to_ids("<|im_end|>")

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=False,
        use_cache=True,
        eos_token_id=im_end_id,
        pad_token_id=tokenizer.pad_token_id,
    )

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

Training Details

Framework: Unsloth + TRL SFTTrainer
LoRA config: r=64, alpha=128, target modules = q/k/v/o/gate/up/down_proj
Optimizer: AdamW 8-bit
LR scheduler: Cosine with warmup ratio 0.03
Batch size: 6 × 8 gradient accumulation = effective 48
train_on_responses_only: Ya (hanya loss pada respons assistant)

Lisensi

Mengikuti lisensi base model: Apache 2.0.