Update

README.md

@@ -1,144 +1,39 @@
-# Panduan Penggunaan Docker untuk Hugging Face Model API
 
-## Struktur File
-```
-your-project/
-├── Dockerfile
-├── docker-compose.yml
-├── requirements.txt
-├── app.py
-├── cache/           # Folder untuk cache model
-└── README.md
-```
 
-## Cara Menjalankan
+# Gema 4B Indonesian Language Model API
 
-### 1. Build dan Jalankan dengan Docker Compose
-```bash
-# Clone atau buat direktori project
-mkdir gema-model-api
-cd gema-model-api
+REST API untuk model bahasa Indonesia berbasis Gema 4B yang dioptimalkan untuk budaya dan pariwisata Indramayu.
 
-# Copy semua file yang telah dibuat
-# Kemudian jalankan:
-docker-compose up --build
-```
+## Endpoints
 
-### 2. Atau Build Manual
-```bash
-# Build image
-docker build -t gema-model-api .
+- `POST /generate` - Generate text
+- `GET /health` - Health check
+- `GET /docs` - API documentation
 
-# Run container
-docker run -p 8000:8000 -v $(pwd)/cache:/root/.cache/huggingface gema-model-api
-```
-
-## Testing API
+## Usage
 
-### 1. Health Check
 ```bash
-curl http://localhost:8000/health
-```
-
-### 2. Generate Text
-```bash
-curl -X POST "http://localhost:8000/generate" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "inputs": "Apa kabar dunia teknologi hari ini?"
-  }'
-```
-
-### 3. Generate dengan Parameter Custom
-```bash
-curl -X POST "http://localhost:8000/generate" \
+curl -X POST "https://dnfs-llm-apiku.hf.space/generate" \
   -H "Content-Type: application/json" \
   -d '{
-    "inputs": "Jelaskan tentang kecerdasan buatan",
-    "max_length": 200,
+    "inputs": "Ceritakan tentang Batik Dermayon",
+    "system_prompt": "ANDA ADALAH AHLI BUDAYA DAN PARIWISATA INDRAMAYU.JAWAB RESPON DENGAN SINGKAT DAN SELALU GUNAKAN BAHASA INDRAMAYU .",
+    "max_tokens": 500,
     "temperature": 0.8,
-    "top_p": 0.95
+    "top_k": 40,
+    "top_p": 0.95,
+    "repeat_penalty": 2,
+    "stop": ["<end_of_turn>"]
   }'
 ```
 
-## Mengakses dari Aplikasi Lain
-
-### Python
-```python
-import requests
-
-url = "http://localhost:8000/generate"
-data = {
-    "inputs": "CONTOH INPUT USER"
-}
-
-response = requests.post(url, json=data)
-result = response.json()
-print(result["generated_text"])
-```
-
-### JavaScript/Node.js
-```javascript
-const response = await fetch('http://localhost:8000/generate', {
-    method: 'POST',
-    headers: {
-        'Content-Type': 'application/json',
-    },
-    body: JSON.stringify({
-        inputs: 'CONTOH INPUT USER'
-    })
-});
-
-const result = await response.json();
-console.log(result.generated_text);
-```
-
-## Dokumentasi API
-Setelah menjalankan container, buka browser dan akses:
-- API Docs: `http://localhost:8000/docs`
-- ReDoc: `http://localhost:8000/redoc`
-
-## Tips Optimasi
-
-### 1. Untuk GPU Support
-Jika Anda memiliki GPU NVIDIA, update `app.py`:
-```python
-# Ubah gpu_layers dari 0 ke jumlah yang sesuai
-gpu_layers=50  # Atau sesuai kemampuan GPU Anda
-```
-
-Dan update `docker-compose.yml`:
-```yaml
-services:
-  gema-model-api:
-    # ... konfigurasi lainnya
-    runtime: nvidia  # Untuk GPU support
-    environment:
-      - NVIDIA_VISIBLE_DEVICES=all
-```
-
-### 2. Untuk Production
-- Gunakan reverse proxy (nginx)
-- Implement authentication
-- Add rate limiting
-- Set up monitoring dan logging
-- Use environment variables untuk konfigurasi
-
-### 3. Memory Management
-Model ini memerlukan RAM yang cukup. Sesuaikan memory limits di docker-compose.yml berdasarkan spesifikasi server Anda.
-
-## Troubleshooting
-
-### Model Loading Issues
-- Pastikan koneksi internet stabil saat pertama kali menjalankan
-- Model akan diunduh otomatis dan disimpan di cache
-- Jika gagal, coba hapus folder cache dan jalankan ulang
-
-### Memory Issues
-- Kurangi `context_length` di app.py
-- Sesuaikan memory limits di docker-compose.yml
-- Gunakan swap file jika diperlukan
+## Parameters
 
-### Port Conflicts
-- Ubah port di docker-compose.yml jika port 8000 sudah digunakan
-- Contoh: `"8080:8000"` untuk menggunakan port 8080
+- `inputs` (required): Input text
+- `system_prompt` (optional): System prompt
+- `max_tokens` (optional): Maximum tokens to generate
+- `temperature` (optional): Sampling temperature
+- `top_k` (optional): Top-k sampling
+- `top_p` (optional): Top-p sampling
+- `repeat_penalty` (optional): Repetition penalty
+- `stop` (optional): Stop sequences

app.py

@@ -16,11 +16,11 @@ app = FastAPI(title="Gema 4B Model API", version="1.0.0")
 class TextRequest(BaseModel):
     inputs: str
     system_prompt: Optional[str] = None
-    max_tokens: Optional[int] = 10
+    max_tokens: Optional[int] = 512
     temperature: Optional[float] = 0.7
     top_k: Optional[int] = 50
     top_p: Optional[float] = 0.9
-    repeat_penalty: Optional[float] = 2
+    repeat_penalty: Optional[float] = 1.1
     stop: Optional[List[str]] = None
 
 # Response model
@@ -90,4 +90,4 @@ async def root():
     return {"message": "Gema 4B Model API", "docs": "/docs"}
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")

dockerfile

@@ -1,27 +1,31 @@
-# Dockerfile
 FROM python:3.10-slim
 
-# Set working directory
 WORKDIR /app
 
 # Install system dependencies
 RUN apt-get update && apt-get install -y \
-    git \
-    curl \
     build-essential \
+    curl \
     && rm -rf /var/lib/apt/lists/*
 
-# Copy requirements first (for better caching)
+# Copy requirements first for better caching
 COPY requirements.txt .
 
 # Install Python dependencies
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy application files
-COPY . .
+# Copy application code
+COPY app.py .
+
+# Create cache directory
+RUN mkdir -p /root/.cache/huggingface
 
 # Expose port
 EXPOSE 8000
 
-# Command to run the application
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+  CMD curl -f http://localhost:8000/health || exit 1
+
+# Run the application
 CMD ["python", "app.py"]

requirements.txt

@@ -1,8 +1,8 @@
-transformers==4.36.0
-torch==2.1.0
 fastapi==0.104.1
-uvicorn==0.24.0
+uvicorn[standard]==0.24.0
+ctransformers==0.2.27
+torch==2.1.0
+transformers==4.36.0
 huggingface-hub==0.19.4
 pydantic==2.5.0
-accelerate==0.25.0
-ctransformers==0.2.27
+accelerate==0.25.0