65 lines
2.4 KiB
Markdown
65 lines
2.4 KiB
Markdown
---
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language:
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- id
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- en
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library_name: transformers
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tags:
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- qwen
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- qwen3
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- instruction-tuning
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- indonesian
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- alpaca
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datasets:
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- Ichsan2895/alpaca-gpt4-indonesian
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pipeline_tag: text-generation
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---
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# Qwen3-4B-Indo-Alpaca
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## Model Description
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`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.
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- **Developer:** caffeinejunkie1
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- **Base Model:** Qwen3-4B
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- **Language(s):** Indonesian (Primary), English
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- **Model Type:** Causal Language Model (SFT)
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- **License:** Apache License 2.0
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## Training Data
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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.
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## Intended Use
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- **Primary Use Cases:** Indonesian text generation, question answering, summarization, and instruction-following.
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- **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).
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## How to Use
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "caffeinejunkie1/Qwen3-4B-Indo-Alpaca"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
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messages = [
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{"role": "system", "content": "Anda adalah asisten AI yang sangat membantu."},
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{"role": "user", "content": "Jelaskan apa itu machine learning dengan bahasa yang sederhana."}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=256
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response) |