AWESOMES/deepseek-coder-android-termux.md at 27e58fa19016adda694d599efc035e6ce279f4fa

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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:

Install Termux & dependencies:

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

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.

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

Use Quantized Models:
- Prefer Q4_K_M or Q5_K_M GGUF models (balance size/quality).
- Find them on TheBloke's Hugging Face.
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).

Clean Cache:

pip cache purge
rm -rf ~/.cache/huggingface

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?

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

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)

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.

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1. Online Access (Web & Playground)

2. API Access

3. Local Deployment (Open-Source Models)

Example: Running with Transformers

4. Supported Features

5. Tips for Best Results

Option 1: Running in Termux (Android)

Requirements:

Steps:

Option 2: Linux Deploy (Full Linux on Android)

Performance Considerations

Alternative: Use the API

1. Model Sizes (Approximate)

2. Storage Requirements in Termux

3. Step-by-Step Installation (Optimized for Storage)

Option A: Small Setup (1.3B Quantized)

Option B: Medium Setup (6.7B Quantized)

4. Storage Optimization Tips

5. Expected Performance

Alternative: Cloud/API

Summary

Recommended Choice: 1.3B Quantized (GGUF)

Why?

How to Set Up 1.3B in Termux

If You Still Want to Try 6.7B

Requirements:

Alternatives

Final Verdict

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Recommended Choice: `1.3B Quantized` (GGUF)