AWESOMES/deepseek-coder-android-termux.md

<p align="center">
<img width="300px" alt="DeepSeek Coder" src="docs/deepseekcoder-logo.png">
</p>

Hello! 👽

**DeepSeek-Coder** is a powerful AI model designed to assist with coding-related tasks, including code generation, debugging, optimization, and more. Here’s how you can use it:  

### **1. Online Access (Web & Playground)**  
You can try **DeepSeek-Coder** for free on the official website:  
🔗 [https://deepseek.com](https://deepseek.com)  

Just type your coding questions (e.g., "Write a Python function to sort a list") and get instant AI-generated responses.  

### **2. API Access**  
If you want to integrate **DeepSeek-Coder** into your applications, you can use the API. Check the official documentation for details.  

### **3. Local Deployment (Open-Source Models)**  
DeepSeek provides open-source models like **DeepSeek-Coder 6.7B/33B** on Hugging Face. You can download and run them locally:  
🔗 [Hugging Face Model Hub](https://huggingface.co/deepseek-ai)  

#### **Example: Running with Transformers**  
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "deepseek-ai/deepseek-coder-6.7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Write a Python function to reverse a string."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### **4. Supported Features**  
👽 **Code Generation** (Python, C++, Java, etc.)  
👽 **Code Debugging & Explanation**  
👽 **Code Optimization & Refactoring**  
👽 **Docstring & Comment Generation**  
👽 **Algorithm & Data Structure Help**  

### **5. Tips for Best Results**  
- Be **specific** in your prompts (e.g., "Write a Python function to merge two sorted lists").  
- Provide **context** if needed (e.g., "I'm working with PyTorch, help me fix this CUDA error").  
- Ask for **explanations** if you want to understand the code.  

---

**Q: Can i run deepseekcoder on android-linux?**

A: Yes! You can run **DeepSeek-Coder** on an **Android-Linux** environment (e.g., using **Termux** or a **Linux chroot**), but with some limitations depending on the model size and your device's hardware. Here's how:

### **Option 1: Running in Termux (Android)**
#### **Requirements**:
- **Termux** (from [F-Droid](https://f-droid.org/en/packages/com.termux/))
- **Python & pip** (install in Termux)
- **A lightweight model** (e.g., `deepseek-coder-1.3b` or quantized versions)

#### **Steps**:
1. **Install Termux & dependencies**:
   ```bash
   pkg update && pkg upgrade
   pkg install python git wget
   pip install transformers torch sentencepiece
   ```
2. **Load the model** (example with `deepseek-coder-1.3b`):
   ```python
   from transformers import AutoModelForCausalLM, AutoTokenizer

   model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name)

   prompt = "Write a Python function to reverse a string."
   inputs = tokenizer(prompt, return_tensors="pt")
   outputs = model.generate(**inputs, max_length=200)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   ```
   - **Note**: Larger models (6.7B/33B) may not fit in RAM on most phones.

3. **Use quantized models** (for lower RAM usage):
   - Try **GGUF-quantized** models via `llama.cpp` (works in Termux):
     ```bash
     pip install llama-cpp-python
     wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q4_K_M.gguf
     ```
     Then load it with:
     ```python
     from llama_cpp import Llama
     llm = Llama(model_path="deepseek-coder-6.7b-instruct.Q4_K_M.gguf")
     print(llm("Write a Python function to reverse a string."))
     ```

### **Option 2: Linux Deploy (Full Linux on Android)**
If your device supports it (e.g., with **Linux Deploy** or **UserLAnd**), you can run a full Linux distro (Ubuntu/Debian) and follow standard Linux instructions:
```bash
# In the Linux environment
git clone https://github.com/deepseek-ai/deepseek-coder
cd deepseek-coder
pip install -r requirements.txt
python run_inference.py
```
- **Pros**: Full GPU support (if your phone has a Linux-compatible GPU).
- **Cons**: Complex setup, requires root/unlocked bootloader for some devices.


### **Performance Considerations**
- **RAM**: 1.3B needs ~3GB RAM, 6.7B needs ~16GB (quantized helps).
- **Storage**: Models take 2GB+ (1.3B) to 40GB+ (33B).
- **Speed**: Expect slow inference on CPUs (~1 token/sec).


### **Alternative: Use the API**
If local execution is too slow, use the **DeepSeek API** (if available) via Python:
```python
import requests
response = requests.post("https://api.deepseek.com/coder", json={"prompt": "Write Python code to reverse a string"})
print(response.json())
```

---

**Q: How storage would be installing deepseek coder thru termux?**

A: Installing **DeepSeek-Coder** in **Termux** (Android) requires careful storage management, as AI models are large. Here's a breakdown of storage needs and optimization tips:

---

### **1. Model Sizes (Approximate)**
| Model                | Disk Space (Full Precision) | Quantized (GGUF) |
|----------------------|----------------------------|------------------|
| DeepSeek-Coder 1.3B  | ~2.5 GB                    | ~0.8 GB (Q4_K_M) |
| DeepSeek-Coder 6.7B  | ~13 GB                     | ~4 GB (Q4_K_M)   |
| DeepSeek-Coder 33B   | ~60 GB+                    | ~20 GB (Q4_K_M)  |

**Note**: Quantized models (e.g., GGUF) reduce size but may lose slight accuracy.

---

### **2. Storage Requirements in Termux**
- **Termux Limits**:  
  - Default storage: ~10-15 GB (varies by device).  
  - External SD card support (needs manual setup).  
- **Recommended for Termux**:  
  - Use **1.3B model** (quantized: ~0.8 GB) or **6.7B quantized** (~4 GB) if your device has space.  
  - Avoid 33B (too large for most phones).  

---

### **3. Step-by-Step Installation (Optimized for Storage)**
#### **Option A: Small Setup (1.3B Quantized)**
```bash
# Install dependencies
pkg update && pkg upgrade
pkg install python git wget
pip install llama-cpp-python

# Download a quantized 1.3B model (GGUF format)
wget https://huggingface.co/TheBloke/deepseek-coder-1.3B-instruct-GGUF/resolve/main/deepseek-coder-1.3b-instruct.Q4_K_M.gguf

# Run inference
python -c "
from llama_cpp import Llama
llm = Llama(model_path='deepseek-coder-1.3b-instruct.Q4_K_M.gguf')
print(llm('Write a Python function to reverse a string.'))
"
```
**Storage Used**: ~1 GB (model + dependencies).  

---

#### **Option B: Medium Setup (6.7B Quantized)**
```bash
# Download 6.7B quantized (~4 GB)
wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q4_K_M.gguf

# Run with reduced RAM usage (--n_ctx 512 lowers memory)
python -c "
from llama_cpp import Llama
llm = Llama(model_path='deepseek-coder-6.7b-instruct.Q4_K_M.gguf', n_ctx=512)
print(llm('Explain bubble sort in Python.'))
"
```
**Storage Used**: ~4.5 GB.  

---

### **4. Storage Optimization Tips**
1. **Use Quantized Models**:  
   - Prefer `Q4_K_M` or `Q5_K_M` GGUF models (balance size/quality).  
   - Find them on [TheBloke's Hugging Face](https://huggingface.co/TheBloke).  

2. **Store Models on SD Card**:  
   - Symlink the model to Termux:  
     ```bash
     ln -s /storage/XXXX-XXXX/deepseek-coder.gguf ~/deepseek-model.gguf
     ```  
     (Replace `XXXX-XXXX` with your SD card path).  

3. **Clean Cache**:  
   ```bash
   pip cache purge
   rm -rf ~/.cache/huggingface
   ```

4. **Use `termux-setup-storage`**:  
   Grants Termux access to phone storage for extra space.  

---

### **5. Expected Performance**
- **1.3B Model**: ~1-2 tokens/sec on CPU (usable for small tasks).  
- **6.7B Model**: ~0.5-1 token/sec (slow but functional).  
- **RAM Usage**: 1.3B needs ~1.5GB RAM; 6.7B needs ~6GB (close Termux apps to free memory).  

---

### **Alternative: Cloud/API**
If storage is critical, use the **DeepSeek API** (if available) instead of local models:  
```python
import requests
response = requests.post("https://api.deepseek.com/v1/chat", json={"model": "deepseek-coder", "messages": [{"role": "user", "content": "Write Fibonacci in Python"}]})
print(response.json())
```

---

### **Summary**
- **For most phones**: 1.3B quantized (~1GB) is practical.  
- **High-end phones**: 6.7B quantized (~4GB) if storage allows.  
- **Avoid 33B** (unless you have a rooted device with 64GB+ free).  

Let me know if you need help setting it up! 

---

**Q: What u suggest for android/samsung galaxy a53 model 1.3B or 6.7B ?**

A: For a **Samsung Galaxy A53** (typically with **6GB RAM** and **128GB storage**), here’s my recommendation for running **DeepSeek-Coder** locally in Termux:

---

### **Recommended Choice: `1.3B Quantized` (GGUF)**
#### **Why?**
1. **RAM Limits**:  
   - The A53 has **6GB RAM**, but Android uses ~2-3GB for the OS, leaving ~3-4GB free.  
   - The **1.3B model** needs ~1.5GB RAM (quantized), while the **6.7B** needs ~6GB (too close to the limit).  

2. **Storage**:  
   - 1.3B quantized: **~0.8GB** (fits easily).  
   - 6.7B quantized: **~4GB** (manageable but tight if you have other apps/media).  

3. **Speed**:  
   - 1.3B: **~2-3 tokens/sec** (usable for small tasks).  
   - 6.7B: **~0.5-1 token/sec** (painfully slow on mobile CPU).  

---

### **How to Set Up 1.3B in Termux**
```bash
# Install dependencies
pkg update && pkg upgrade
pkg install python git wget
pip install llama-cpp-python

# Download the quantized 1.3B model (Q4_K_M)
wget https://huggingface.co/TheBloke/deepseek-coder-1.3B-instruct-GGUF/resolve/main/deepseek-coder-1.3b-instruct.Q4_K_M.gguf

# Run inference (adjust n_ctx for RAM savings)
python -c "
from llama_cpp import Llama
llm = Llama(model_path='deepseek-coder-1.3b-instruct.Q4_K_M.gguf', n_ctx=512)
print(llm('Write a Python function to check if a number is prime.'))
"
```

---

### **If You Still Want to Try 6.7B**
#### **Requirements**:
- Close all background apps.  
- Enable **swap memory** (if rooted) or use **zRAM** (advanced Termux tweaks).  
- Expect **very slow speeds** (~1 word every 2 seconds).  

```bash
wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q4_K_M.gguf
python -c "
from llama_cpp import Llama
llm = Llama(model_path='deepseek-coder-6.7b-instruct.Q4_K_M.gguf', n_ctx=256)  # lower ctx to save RAM
print(llm('Explain Python decorators briefly.'))
"
```

---

### **Alternatives**
1. **Use the API** (if available):  
   No storage/RAM limits, but requires internet.  
   ```python
   import requests
   response = requests.post("https://api.deepseek.com/coder", json={"prompt": "Write a Python calculator"})
   print(response.text)
   ```

2. **Remote Server**:  
   Run the model on a PC/Raspberry Pi and access it from your phone via `curl`.  

---

### **Final Verdict**
- **For daily use**: Stick with **1.3B quantized**. It’s the only practical option for your A53.  
- **For experiments**: Try 6.7B, but expect crashes/slowness.  
- **For heavy tasks**: Use the **DeepSeek web app** or API.