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Model: caffeinejunkie1/Qwen3-4B-Indo-Alpaca
Source: Original Platform

README.md

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+---
+language:
+- id
+- en
+library_name: transformers
+tags:
+- qwen
+- qwen3
+- instruction-tuning
+- indonesian
+- alpaca
+datasets:
+- Ichsan2895/alpaca-gpt4-indonesian
+pipeline_tag: 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](https://huggingface.co/datasets/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
+```python
+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)