/TinyLLM

Setup and run a local LLM and Chatbot using consumer grade hardware.

Primary LanguagePythonMIT LicenseMIT

TinyLLM

TinyLLM? Yes, the name is a bit of a contradiction, but it means well. It's all about putting a large language model (LLM) on a tiny system that still delivers acceptable performance.

This project helps you build a small locally hosted LLM with a ChatGPT-like web interface using consumer grade hardware. To read more about my research with llama.cpp and LLMs, see research.md.

Key Features

  • Supports multiple LLMs (see list below)
  • Builds a local OpenAI API web service via llama-cpp-python or vLLM.
  • Serves up a Chatbot web interface with customizable prompts, accessing external websites (URLs), vector databases and other sources (e.g. news, stocks, weather).

Hardware Requirements

  • CPU: Intel, AMD or Apple Silicon
  • Memory: 8GB+ DDR4
  • Disk: 128G+ SSD
  • GPU: NVIDIA (e.g. GTX 1060 6GB, RTX 3090 24GB) or Apple M1/M2
  • OS: Ubuntu Linux, MacOS
  • Software: Python 3, CUDA Version: 12.2

Quickstart

TODO - Quick start setup script.

Manual Setup

# Clone the project
git clone https://github.com/jasonacox/TinyLLM.git
cd TinyLLM

Run a Local LLM

To run a local LLM, you will need an inference server for the model. This project recommends two options: vLLM and llama-cpp-python. Both provide a built-in OpenAI API compatible web server that will make it easier for you to integrate with other tools.

Llama-cpp-python Server (Option 1)

The llama-cpp-python's OpenAI API capatible web server is easy to set up and use. It runs optimized GGUF models that work well on many consumer grade GPUs with small amounts of VRAM. A downside with this server is that it can only handle one session/prompt at a time. The steps below outline how to setup and run the server via command line. Read the details in llmserver to see how to set it up as a persistent service or docker container on your Linux host.

# Uninstall any old version of llama-cpp-python
pip3 uninstall llama-cpp-python -y

# Linux Target with Nvidia CUDA support
CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip3 install llama-cpp-python==0.2.27 --no-cache-dir
CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip3 install llama-cpp-python[server]==0.2.27 --no-cache-dir

# MacOS Target with Apple Silicon M1/M2
CMAKE_ARGS="-DLLAMA_METAL=on" pip3 install -U llama-cpp-python --no-cache-dir
pip3 install 'llama-cpp-python[server]'

# Download Models from HuggingFace
cd llmserver/models

# Get the Mistral 7B GGUF Q-5bit model Q5_K_M and Meta LLaMA-2 7B GGUF Q-5bit model Q5_K_M
wget https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q5_K_M.gguf
wget https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q5_K_M.gguf

# Run Test - API Server
python3 -m llama_cpp.server \
    --model ./models/mistral-7b-instruct-v0.1.Q5_K_M.gguf \
    --host localhost \
    --n_gpu_layers 99 \
    --n_ctx 2048 \
    --chat_format llama-2

vLLM Server (Option 2)

vLLM offers a robust OpenAI API compatible web server that supports multiple simultaneous inference threads (sessions). It automatically downloads the models you specifdy from HuggingFace and runs extremely well in containers. vLLM requires GPUs with more VRAM since it uses non-quantized models. AWQ models are also available and more optimizations are underway in the project to reduce the memory footprint. Note, for GPUs with a compute capability of 6 or less, Pascal architecture (see GPU table), follow details here instead.

# Build Container
cd vllm
./build.sh 

# Make a Directory to store Models
mkdir models

# Edit run.sh or run-awq.sh to pull the model you want to use. Mistral is set by default.
# Run the Container - This will download the model on the first run
./run.sh  

# The trailing logs will be displayed so you can see the progress. Use ^C to exit without
# stopping the container.

Run a Chatbot

The TinyLLM Chatbot is a simple web based python flask app that allows you to chat with an LLM using the OpenAI API. It supports multiple sessions and remembers your conversational history. Some RAG (Retrieval Augmented Generation) features including:

  • Summarizing external websites and PDFs (paste a URL in chat window)
  • List top 10 headlines from current news (use /news)
  • Display company stock symbol and current stock price (use /stock <company>)
  • Provide current weather conditions (use /weather <location>)
  • Use a vector databases for RAG queries - see RAG page for details
# Move to chatbot folder
cd ../chatbot
touch prompts.json

# Pull and run latest container - see run.sh
docker run \
    -d \
    -p 5000:5000 \
    -e PORT=5000 \
    -e OPENAI_API_BASE="http://localhost:8000/v1" \
    -e LLM_MODEL="tinyllm" \
    -e USE_SYSTEM="false" \
    -e SENTENCE_TRANSFORMERS_HOME=/app/.tinyllm \
    -v $PWD/.tinyllm:/app/.tinyllm \
    --name chatbot \
    --restart unless-stopped \
    jasonacox/chatbot

Example Session

Open http://localhost:5000 - Example session:

image

Read URLs

If a URL is pasted in the text box, the chatbot will read and summarize it.

image

Current News

The /news command will fetch the latest news and have the LLM summarize the top ten headlines. It will store the raw feed in the context prompt to allow follow-up questions.

image

Manual Setup

You can also test the chatbot server without docker using the following.

# Install required packages
pip3 install fastapi uvicorn python-socketio jinja2 openai bs4 pypdf requests lxml aiohttp

# Run the chatbot web server
python3 server.py

LLM Models

Here are some suggested models that work well with llmserver (llama-cpp-python). You can test other models and different quantization, but in my experiments, the Q5_K_M models performed the best. Below are the download links from HuggingFace as well as the model card's suggested context length size and chat prompt mode.

LLM Quantized Link to Download Context Length Chat Prompt Mode
7B Models
Mistral v0.1 7B 5-bit mistral-7b-instruct-v0.1.Q5_K_M.gguf 4096 llama-2
Llama-2 7B 5-bit llama-2-7b-chat.Q5_K_M.gguf 2048 llama-2
Mistrallite 32K 7B 5-bit mistrallite.Q5_K_M.gguf 16384 mistrallite (can be glitchy)
10B Models
Nous-Hermes-2-SOLAR 10.7B 5-bit nous-hermes-2-solar-10.7b.Q5_K_M.gguf 4096 chatml
13B Models
Claude2 trained Alpaca 13B 5-bit claude2-alpaca-13b.Q5_K_M.gguf 2048 chatml
Llama-2 13B 5-bit llama-2-13b-chat.Q5_K_M.gguf 2048 llama-2
Vicuna 13B v1.5 5-bit vicuna-13b-v1.5.Q5_K_M.gguf 2048 vicuna
Mixture-of-Experts (MoE) Models
Hai's Mixtral 11Bx2 MoE 19B 5-bit mixtral_11bx2_moe_19b.Q5_K_M.gguf 4096 chatml
Mixtral-8x7B v0.1 3-bit Mixtral-8x7B-Instruct-v0.1-GGUF 4096 llama-2
Mixtral-8x7B v0.1 4-bit Mixtral-8x7B-Instruct-v0.1-GGUF 4096 llama-2

Here are some suggested models that work well with vLLM.

LLM Quantized Link to Download Context Length
Mistral v0.1 7B None mistralai/Mistral-7B-Instruct-v0.1 32k
Mistral v0.2 7B None mistralai/Mistral-7B-Instruct-v0.2 32k
Mistral v0.1 7B AWQ AWQ TheBloke/Mistral-7B-Instruct-v0.1-AWQ 32k
Mixtral-8x7B None mistralai/Mixtral-8x7B-Instruct-v0.1 32k
Meta Llama-3 8B None meta-llama/Meta-Llama-3-8B-Instruct 8k
meta-llama/Meta-Llama-3-8B-Instruct

References