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caffeinejunkie1
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caffeinejunkie1
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Qwen3-4B-GRPO-Indo-Alpaca is an aligned instruction-following model tailored for the Indonesian language. While relying on the Qwen3-4B architecture, this version goes beyond standard supervised fine-tuning by utilizing Group Relative Policy Optimization (GRPO). This reinforcement learning technique optimizes the model's generation policy to improve reasoning clarity, reduce hallucinations, and better align with human preferences in Indonesian contexts.
Indo-Alpaca variant.python
from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "caffeinejunkie1/Qwen3-4B-GRPO-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 logis dan akurat."}, {"role": "user", "content": "Berikan 3 alasan utama mengapa arsitektur MVVM sering digunakan dalam pengembangan aplikasi."}] 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=512, temperature=0.7, top_p=0.9)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)from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "caffeinejunkie1/Qwen3-4B-GRPO-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 logis dan akurat."}, {"role": "user", "content": "Berikan 3 alasan utama mengapa arsitektur MVVM sering digunakan dalam pengembangan aplikasi."}] 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=512, temperature=0.7, top_p=0.9)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)