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.