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deepseek-coder-android-termux.md
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deepseek-coder-android-termux.md
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Hello! 👽
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**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:
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### **1. Online Access (Web & Playground)**
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You can try **DeepSeek-Coder** for free on the official website:
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🔗 [https://deepseek.com](https://deepseek.com)
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Just type your coding questions (e.g., "Write a Python function to sort a list") and get instant AI-generated responses.
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### **2. API Access**
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If you want to integrate **DeepSeek-Coder** into your applications, you can use the API. Check the official documentation for details.
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### **3. Local Deployment (Open-Source Models)**
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DeepSeek provides open-source models like **DeepSeek-Coder 6.7B/33B** on Hugging Face. You can download and run them locally:
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🔗 [Hugging Face Model Hub](https://huggingface.co/deepseek-ai)
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#### **Example: Running with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "deepseek-ai/deepseek-coder-6.7b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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input_text = "Write a Python function to reverse a string."
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inputs = tokenizer(input_text, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=200)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### **4. Supported Features**
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👽 **Code Generation** (Python, C++, Java, etc.)
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👽 **Code Debugging & Explanation**
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👽 **Code Optimization & Refactoring**
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👽 **Docstring & Comment Generation**
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👽 **Algorithm & Data Structure Help**
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### **5. Tips for Best Results**
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- Be **specific** in your prompts (e.g., "Write a Python function to merge two sorted lists").
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- Provide **context** if needed (e.g., "I'm working with PyTorch, help me fix this CUDA error").
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- Ask for **explanations** if you want to understand the code.
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---
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**Q: Can i run deepseekcoder on android-linux?**
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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:
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### **Option 1: Running in Termux (Android)**
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#### **Requirements**:
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- **Termux** (from [F-Droid](https://f-droid.org/en/packages/com.termux/))
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- **Python & pip** (install in Termux)
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- **A lightweight model** (e.g., `deepseek-coder-1.3b` or quantized versions)
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#### **Steps**:
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1. **Install Termux & dependencies**:
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```bash
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pkg update && pkg upgrade
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pkg install python git wget
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pip install transformers torch sentencepiece
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```
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2. **Load the model** (example with `deepseek-coder-1.3b`):
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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prompt = "Write a Python function to reverse a string."
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=200)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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- **Note**: Larger models (6.7B/33B) may not fit in RAM on most phones.
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3. **Use quantized models** (for lower RAM usage):
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- Try **GGUF-quantized** models via `llama.cpp` (works in Termux):
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```bash
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pip install llama-cpp-python
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wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q4_K_M.gguf
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```
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Then load it with:
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="deepseek-coder-6.7b-instruct.Q4_K_M.gguf")
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print(llm("Write a Python function to reverse a string."))
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```
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### **Option 2: Linux Deploy (Full Linux on Android)**
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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:
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```bash
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# In the Linux environment
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git clone https://github.com/deepseek-ai/deepseek-coder
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cd deepseek-coder
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pip install -r requirements.txt
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python run_inference.py
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```
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- **Pros**: Full GPU support (if your phone has a Linux-compatible GPU).
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- **Cons**: Complex setup, requires root/unlocked bootloader for some devices.
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### **Performance Considerations**
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- **RAM**: 1.3B needs ~3GB RAM, 6.7B needs ~16GB (quantized helps).
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- **Storage**: Models take 2GB+ (1.3B) to 40GB+ (33B).
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- **Speed**: Expect slow inference on CPUs (~1 token/sec).
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### **Alternative: Use the API**
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If local execution is too slow, use the **DeepSeek API** (if available) via Python:
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```python
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import requests
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response = requests.post("https://api.deepseek.com/coder", json={"prompt": "Write Python code to reverse a string"})
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print(response.json())
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```
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---
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**Q: How storage would be installing deepseek coder thru termux?**
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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:
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---
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### **1. Model Sizes (Approximate)**
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| Model | Disk Space (Full Precision) | Quantized (GGUF) |
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|----------------------|----------------------------|------------------|
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| DeepSeek-Coder 1.3B | ~2.5 GB | ~0.8 GB (Q4_K_M) |
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| DeepSeek-Coder 6.7B | ~13 GB | ~4 GB (Q4_K_M) |
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| DeepSeek-Coder 33B | ~60 GB+ | ~20 GB (Q4_K_M) |
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**Note**: Quantized models (e.g., GGUF) reduce size but may lose slight accuracy.
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---
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### **2. Storage Requirements in Termux**
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- **Termux Limits**:
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- Default storage: ~10-15 GB (varies by device).
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- External SD card support (needs manual setup).
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- **Recommended for Termux**:
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- Use **1.3B model** (quantized: ~0.8 GB) or **6.7B quantized** (~4 GB) if your device has space.
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- Avoid 33B (too large for most phones).
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---
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### **3. Step-by-Step Installation (Optimized for Storage)**
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#### **Option A: Small Setup (1.3B Quantized)**
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```bash
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# Install dependencies
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pkg update && pkg upgrade
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pkg install python git wget
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pip install llama-cpp-python
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# Download a quantized 1.3B model (GGUF format)
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wget https://huggingface.co/TheBloke/deepseek-coder-1.3B-instruct-GGUF/resolve/main/deepseek-coder-1.3b-instruct.Q4_K_M.gguf
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# Run inference
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python -c "
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from llama_cpp import Llama
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llm = Llama(model_path='deepseek-coder-1.3b-instruct.Q4_K_M.gguf')
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print(llm('Write a Python function to reverse a string.'))
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"
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```
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**Storage Used**: ~1 GB (model + dependencies).
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---
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#### **Option B: Medium Setup (6.7B Quantized)**
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```bash
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# Download 6.7B quantized (~4 GB)
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wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q4_K_M.gguf
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# Run with reduced RAM usage (--n_ctx 512 lowers memory)
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python -c "
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from llama_cpp import Llama
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llm = Llama(model_path='deepseek-coder-6.7b-instruct.Q4_K_M.gguf', n_ctx=512)
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print(llm('Explain bubble sort in Python.'))
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"
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```
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**Storage Used**: ~4.5 GB.
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---
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### **4. Storage Optimization Tips**
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1. **Use Quantized Models**:
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- Prefer `Q4_K_M` or `Q5_K_M` GGUF models (balance size/quality).
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- Find them on [TheBloke's Hugging Face](https://huggingface.co/TheBloke).
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2. **Store Models on SD Card**:
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- Symlink the model to Termux:
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```bash
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ln -s /storage/XXXX-XXXX/deepseek-coder.gguf ~/deepseek-model.gguf
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```
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(Replace `XXXX-XXXX` with your SD card path).
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3. **Clean Cache**:
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```bash
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pip cache purge
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rm -rf ~/.cache/huggingface
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```
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4. **Use `termux-setup-storage`**:
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Grants Termux access to phone storage for extra space.
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---
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### **5. Expected Performance**
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- **1.3B Model**: ~1-2 tokens/sec on CPU (usable for small tasks).
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- **6.7B Model**: ~0.5-1 token/sec (slow but functional).
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- **RAM Usage**: 1.3B needs ~1.5GB RAM; 6.7B needs ~6GB (close Termux apps to free memory).
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---
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### **Alternative: Cloud/API**
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If storage is critical, use the **DeepSeek API** (if available) instead of local models:
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```python
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import requests
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response = requests.post("https://api.deepseek.com/v1/chat", json={"model": "deepseek-coder", "messages": [{"role": "user", "content": "Write Fibonacci in Python"}]})
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print(response.json())
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```
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---
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### **Summary**
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- **For most phones**: 1.3B quantized (~1GB) is practical.
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- **High-end phones**: 6.7B quantized (~4GB) if storage allows.
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- **Avoid 33B** (unless you have a rooted device with 64GB+ free).
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Let me know if you need help setting it up!
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---
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**Q: What u suggest for android/samsung galaxy a53 model 1.3B or 6.7B ?**
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A: For a **Samsung Galaxy A53** (typically with **6GB RAM** and **128GB storage**), here’s my recommendation for running **DeepSeek-Coder** locally in Termux:
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---
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### **Recommended Choice: `1.3B Quantized` (GGUF)**
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#### **Why?**
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1. **RAM Limits**:
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- The A53 has **6GB RAM**, but Android uses ~2-3GB for the OS, leaving ~3-4GB free.
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- The **1.3B model** needs ~1.5GB RAM (quantized), while the **6.7B** needs ~6GB (too close to the limit).
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2. **Storage**:
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- 1.3B quantized: **~0.8GB** (fits easily).
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- 6.7B quantized: **~4GB** (manageable but tight if you have other apps/media).
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3. **Speed**:
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- 1.3B: **~2-3 tokens/sec** (usable for small tasks).
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- 6.7B: **~0.5-1 token/sec** (painfully slow on mobile CPU).
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---
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### **How to Set Up 1.3B in Termux**
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```bash
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# Install dependencies
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pkg update && pkg upgrade
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pkg install python git wget
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pip install llama-cpp-python
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# Download the quantized 1.3B model (Q4_K_M)
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wget https://huggingface.co/TheBloke/deepseek-coder-1.3B-instruct-GGUF/resolve/main/deepseek-coder-1.3b-instruct.Q4_K_M.gguf
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# Run inference (adjust n_ctx for RAM savings)
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python -c "
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from llama_cpp import Llama
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llm = Llama(model_path='deepseek-coder-1.3b-instruct.Q4_K_M.gguf', n_ctx=512)
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print(llm('Write a Python function to check if a number is prime.'))
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"
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```
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---
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### **If You Still Want to Try 6.7B**
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#### **Requirements**:
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- Close all background apps.
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- Enable **swap memory** (if rooted) or use **zRAM** (advanced Termux tweaks).
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- Expect **very slow speeds** (~1 word every 2 seconds).
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```bash
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wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q4_K_M.gguf
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python -c "
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from llama_cpp import Llama
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llm = Llama(model_path='deepseek-coder-6.7b-instruct.Q4_K_M.gguf', n_ctx=256) # lower ctx to save RAM
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print(llm('Explain Python decorators briefly.'))
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"
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```
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---
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### **Alternatives**
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1. **Use the API** (if available):
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No storage/RAM limits, but requires internet.
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```python
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import requests
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response = requests.post("https://api.deepseek.com/coder", json={"prompt": "Write a Python calculator"})
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print(response.text)
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```
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2. **Remote Server**:
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Run the model on a PC/Raspberry Pi and access it from your phone via `curl`.
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---
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### **Final Verdict**
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- **For daily use**: Stick with **1.3B quantized**. It’s the only practical option for your A53.
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- **For experiments**: Try 6.7B, but expect crashes/slowness.
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- **For heavy tasks**: Use the **DeepSeek web app** or API.
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