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Model: Atikarahmanda/Qwen3-4B-SFT-Multitask
Source: Original Platform

README.md

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+---
+base_model: aitf-kpm-ugm/Qwen3-4B-CPT-Base
+library_name: transformers
+language:
+- id
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- qwen3
+- bahasa-indonesia
+- sft
+- multitask
+- sentiment-analysis
+- kategorisasi
+- alpaca
+- lora-merged
+datasets:
+- custom
+---
+
+# Qwen3-4B SFT — Multitask Bahasa Indonesia
+
+Model hasil fine-tuning (LoRA, sudah di-**merge** ke base weights) dari
+[`aitf-kpm-ugm/Qwen3-4B-CPT-Base`](https://huggingface.co/aitf-kpm-ugm/Qwen3-4B-CPT-Base) untuk berbagai tugas NLP **Bahasa Indonesia**.
+
+---
+
+## Detail Model
+
+| 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 |
+
+---
+
+## Task yang Didukung
+
+1. **Sentimen Analysis** — klasifikasi positif / netral / negatif
+2. **Justifier** — klasifikasi is_relevant: true / false
+3. **Kategorisasi Issue** — klasifikasi sub_category_label
+
+---
+
+## Cara Pakai
+
+````python
+import torch
+from 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).