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Atikarahmanda
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Model Details
Model Provider
Atikarahmanda
Model Tree
Base
aitf-kpm-ugm/Qwen3-4B-CPT-Base
Fine-tuned
this model
Input Modalities
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Output Modalities
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Supported Functionality
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| 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 | Alpaca |
| Precision | bfloat16 |
| Max seq length | 2048 |
| Training epochs | 3 |
| Best checkpoint | step 2400 |
| Best val loss | 0.3566 |
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMREPO = "Atikarahmanda/Qwen3-4B-SFT-Multitask"tokenizer = AutoTokenizer.from_pretrained(REPO)model = AutoModelForCausalLM.from_pretrained(REPO,torch_dtype=torch.bfloat16,device_map="auto",)model.eval()messages = [{"role": "system", "content": "Sistem prompt sesuai task."},{"role": "user", "content": "Input artikel di sini."},]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]with torch.no_grad():out = model.generate(**inputs,max_new_tokens=128,do_sample=False,eos_token_id=tokenizer.eos_token_id,pad_token_id=tokenizer.pad_token_id,)print(tokenizer.decode(out[0, input_len:], skip_special_tokens=True))```---## Training Details- **Framework**: Unsloth + TRL SFTTrainer- **LoRA config**: r=64, alpha=128- **Optimizer**: AdamW 8-bit- **LR scheduler**: Cosine, warmup ratio 0.03- **Effective batch size**: 6 x 8 = 48- **train_on_responses_only**: Ya---## LisensiMengikuti lisensi base model: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).