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Atikarahmanda
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Model Details
Model Provider
Atikarahmanda
Model Tree
aitf-kpm-ugm/Qwen3-4B-CPT-Base
Input Modalities
Output Modalities
Supported Functionality
| 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, AutoModelForCausalLM REPO = "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 --- ## Lisensi Mengikuti lisensi base model: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM REPO = "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 --- ## Lisensi Mengikuti lisensi base model: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).