# Selecting a Local Large Language Model

## Step 1: Determine Your Requirements

1. **Purpose**:
   - What will you use the model for? (e.g., general-purpose assistant, code completion, specific tasks)

   All of the above.

2. **Hardware Specifications**:
   - **GPU**: Does your laptop have a dedicated GPU (e.g., NVIDIA with CUDA support)?

   Yes it does. It's an older generation NVIDIA GPU.

   - **RAM**: How much system memory is available?

   32 gigs of ram

   - **Disk Space**: How much storage can you allocate for the model and its dependencies?

   1 terabyte or more

3. **Ease of Use**:
   - Are you comfortable setting up frameworks like Docker or Python environments?

    Yes. Please keep everything in docker including python.


4. **Language Model Size**:
   - Do you need a lightweight model (<10 GB) for responsiveness or a large model for complex tasks?

   How about a mixture and I can switch them based on the task?

## Step 2: Evaluate Available Models (Optional for Editing)

Here are some options to consider:
- **GPT-Based Models**: GPT-J, GPT-NeoX
- **LLaMA (Meta)**: LLaMA 2 (7B, 13B, 70B)
- **Falcon**: Falcon 7B / 40B
- **Mistral**: Lightweight and efficient 7B
- **Vicuna**: Fine-tuned for conversational tasks

I have ollama installed and I've downloaded a few models. Here is the ollama models I have so far:

codellama:7b-code                  8df0a30bb1e6    3.8 GB    44 hours ago
codellama:7b-instruct              8fdf8f752f6e    3.8 GB    44 hours ago
maryasov/llama3.1-cline:latest     2ecd12227dcf    4.7 GB    45 hours ago
hhao/qwen2.5-coder-tools:latest    1fbf62f22cd4    4.7 GB    45 hours ago
deepseek-coder:6.7b-base           585a5cb3b219    3.8 GB    3 weeks ago
qwen2.5-coder:7b-base              bd8755145f1c    4.7 GB    3 weeks ago
starcoder:7b                       53fdbc3a2006    4.3 GB    3 weeks ago
deepseek-coder:base                3b417b786925    776 MB    3 weeks agoo

## Step 3: Tools for Running Models Locally

1. Text-Generation-WebUI: A user-friendly web interface for loading models.
2. Hugging Face Transformers: A Python-based framework for managing models.
3. GGML / GPTQ (Quantized Models): Lightweight alternatives with reduced precision.

Ok. I have both my local laptop and a big beefy server running Cloudron. I have Open-WebUI deployed in Cloudron. I would like to use both the local laptop and my remote server.

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Edit this document with your answers or add any additional details you'd like me to consider. Upload the updated file back, and I'll guide you further!
