Qwen3-4B-Indo-Alpaca/README.md

language, library_name, tags, datasets, pipeline_tag

language	library_name	tags	datasets	pipeline_tag
id en	transformers	qwen qwen3 instruction-tuning indonesian alpaca	Ichsan2895/alpaca-gpt4-indonesian	text-generation

Qwen3-4B-Indo-Alpaca

Model Description

Qwen3-4B-Indo-Alpaca is an instruction-tuned language model designed for Indonesian natural language processing tasks. Built upon the Qwen3-4B base model, it has been fine-tuned using Supervised Fine-Tuning (SFT) on a translated Indonesian Alpaca-GPT4 dataset. This model is optimized to understand instructions, answer questions, and assist with general conversational tasks in Indonesian.

• Developer: caffeinejunkie1
• Base Model: Qwen3-4B
• Language(s): Indonesian (Primary), English
• Model Type: Causal Language Model (SFT)
• License: Apache License 2.0

Training Data

The model was fine-tuned exclusively on the Ichsan2895/alpaca-gpt4-indonesian dataset. This dataset contains instruction-response pairs originally generated by GPT-4 and translated into Indonesian, providing high-quality demonstrations for instruction following.

Intended Use

• Primary Use Cases: Indonesian text generation, question answering, summarization, and instruction-following.
• Out-of-Scope: Advanced mathematical reasoning, highly specialized medical/legal advice, or tasks requiring up-to-the-minute real-world knowledge (as the model is constrained by its training data cutoff).

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "caffeinejunkie1/Qwen3-4B-Indo-Alpaca"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "system", "content": "Anda adalah asisten AI yang sangat membantu."},
    {"role": "user", "content": "Jelaskan apa itu machine learning dengan bahasa yang sederhana."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=256
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)