Update README.md

README.md

@@ -0,0 +1,116 @@
+---
+license: apache-2.0
+base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
+tags:
+- unsloth
+- gemma
+- peft
+- lora
+- indonesia
+- fine-tuned
+- indramayu
+language:
+- id
+---
+
+# Model Gemma-3-1B Fine-tuned - Mango City Edition
+
+This model is fine-tuned from `unsloth/gemma-3-1b-it-unsloth-bnb-4bit` using the LoRA (Low-Rank Adaptation) method with a specific dataset focused on local knowledge (such as "Kota Mangga" - Indramayu, the Mango City).
+
+This model was trained using Unsloth for significant memory and speed efficiency.
+
+## Repository Structure
+
+This repository contains the model in two formats for flexibility:
+
+```
+/
+├── config.json
+├── model.safetensors
+├── tokenizer.json
+├── tokenizer_config.json
+├── special_tokens_map.json
+├── generation_config.json
+└── adapter/                 # adapter LoRA
+    ├── adapter_config.json
+    ├── adapter_model.safetensors
+    └── ...
+```
+
+## How to Use
+
+### 1. Using the Merged Model
+
+This is the easiest way to use the model. You will be loading a ready-to-use merged model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "Alamaks/Mangga-2-1b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16, # Gunakan float16 untuk efisiensi
+    device_map="auto"
+)
+
+# Contoh penggunaan
+inputs = tokenizer("Siapakah bupati Indramayu saat ini?", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### 2. Using the LoRA Adapter
+
+If you want to apply the adapter to the base model manually.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+base_model_id = "unsloth/gemma-3-1b-it"
+adapter_id = "Alamaks/Mangga-2-1b"
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load the tokenizer from this repo (as there might be new tokens)
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+
+# Apply the adapter
+model = PeftModel.from_pretrained(
+    base_model,
+    adapter_id,
+    subfolder="adapter" # Don't forget the subfolder
+)
+
+# Example usage
+inputs = tokenizer("Jelaskan tentang julukan Indramayu sebagai Kota Mangga.", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Model Details
+
+**Model Hierarchy:**
+- **Original Model:**  `google/gemma-3-1b-it` (Google Gemma-3-1B Instruct)
+- **Unsloth Base Model:** `unsloth/gemma-3-1b-it` (Optimized version of the Google model)
+- **Training Model:** `unsloth/gemma-3-1b-it-unsloth-bnb-4bit` (4-bit quantized version for fine-tuning)
+- **Final Model:** `Alamaks/Mangga-2-1b` (The fine-tuned model)
+
+**Specifications:**
+- **Method:** LoRA (Low-Rank Adaptation) via Unsloth
+- **Language:** Indramayu
+- **Upload Precision:** 16-bit
+
+
+## Lisensi
+
+Apache 2.0