AWESOMES/deepseek-coder-android-termux.md

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

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

Example: Running with Transformers

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)
• Python & pip (install in Termux)
• A lightweight model (e.g., deepseek-coder-1.3b or quantized versions)

Steps:

1. Install Termux & dependencies:

   pkg update && pkg upgrade
   pkg install python git wget
   pip install transformers torch sentencepiece
   

2. Load the model (example with deepseek-coder-1.3b):

   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):

     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:

     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:

# 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:

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)

# 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)

# 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.

2. Store Models on SD Card:

   • Symlink the model to Termux:

     ln -s /storage/XXXX-XXXX/deepseek-coder.gguf ~/deepseek-model.gguf
     

     (Replace XXXX-XXXX with your SD card path).

3. Clean Cache:

   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:

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

# 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).

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.

   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.