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Using local models within DSPy

DSPy supports various methods including built-in wrappers, server integration, and external package integration for model loading. This documentation provides a concise introduction on how to load in models within DSPy extending these capabilities for your specific needs.

Local Model Loaders

Loaders Jump To
HFModel HFModel Section
Cohere Cohere Section
TGI TGI Section
VLLM VLLM Section

HFModel

Initialize HFModel within your program with the desired model to load in. Here's an example call:

llama = dspy.HFModel(model = 'meta-llama/Llama-2-7b-hf')

Text-Generation-Inference Server

Prerequisites

  • Docker must be installed on your system. If you don't have Docker installed, you can get it from here.

Setting up the Text-Generation-Inference Server

  1. Clone the Text-Generation-Inference repository from GitHub by executing the following command:

    git clone https://github.com/huggingface/text-generation-inference.git

  2. Change into the cloned repository directory:

    cd text-generation-inference

  3. Execute the Docker command under the "Get Started" section to run the server:

    model=meta-llama/Llama-2-7b-hf # set to the specific Hugging Face model ID you wish to use.
num_shard=2 # set to the number of shards you wish to use.
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:0.9 --model-id $model --num-shard $num_shard

    This command will start the server and make it accessible at http://localhost:8080.

If you want to connect to Meta Llama 2 models, make sure to use version 9.3 (or higher) of the docker image (ghcr.io/huggingface/text-generation-inference:0.9.3) and pass in your huggingface token as an environment variable.

docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data -e HUGGING_FACE_HUB_TOKEN={your_token} ghcr.io/huggingface/text-generation-inference:0.9.3 --model-id $model --num-shard $num_shard

Sending requests to the server

After setting up the text-generation-inference server and ensuring that it displays "Connected" when it's running, you can interact with it using the HFClientTGI.

Initialize the HFClientTGI within your program with the desired parameters. Here is an example call:

lm = dspy.HFClientTGI(model="meta-llama/Llama-2-7b-hf", port=8080, url="http://localhost")

Customize the model, port, and url according to your requirements. The model parameter should be set to the specific Hugging Face model ID you wish to use.

FAQs

  1. If your model doesn't require any shards, you still need to set a value for num_shard, but you don't need to include the parameter --num-shard on the command line.

  2. If your model runs into any "token exceeded" issues, you can set the following parameters on the command line to adjust the input length and token limit:

    • --max-input-length: Set the maximum allowed input length for the text.
    • --max-total-tokens: Set the maximum total tokens allowed for text generation.

Please refer to the official Text-Generation-Inference repository for more detailed information and documentation.

vLLM Server

Setting up the vLLM Server

Follow these steps to set up the vLLM Server:

  1. Build the server from source by following the instructions provided in the Build from Source guide.

  2. Start the server by running the following command, and specify your desired model, host, and port using the appropriate arguments. The default server address is http://localhost:8000.

    Example command:

    python -m vllm.entrypoints.api_server --model mosaicml/mpt-7b --port 8000

This will launch the vLLM server.

Sending requests to the vLLM server

After setting up the vLLM server and ensuring that it displays "Connected" when it's running, you can interact with it using the HFClientVLLM.

Initialize the HFClientVLLM within your program with the desired parameters. Here is an example call:

lm = dspy.HFClientVLLM(model="mosaicml/mpt-7b", port=8000, url="http://localhost")

Customize the model, port, url, and max_tokens according to your requirements. The model parameter should be set to the specific Hugging Face model ID you wish to use.

Please refer to the official vLLM repository for more detailed information and documentation.

MLC LLM

Prerequisites

  1. Install the required packages using the following commands:

    pip install --no-deps --pre --force-reinstall mlc-ai-nightly-cu118 mlc-chat-nightly-cu118 -f https://mlc.ai/wheels
pip install transformers
git lfs install

    Adjust the pip wheels according to your OS/platform by referring to the provided commands in MLC packages.

Running MLC Llama-2 models

  1. Create a directory for prebuilt models:

    mkdir -p dist/prebuilt

  2. Clone the necessary libraries from the repository:

    git clone https://github.com/mlc-ai/binary-mlc-llm-libs.git dist/prebuilt/lib
cd dist/prebuilt

  3. Choose a Llama-2 model from MLC LLMs and clone the model repository:

    git clone https://huggingface.co/mlc-ai/mlc-chat-Llama-2-7b-chat-hf-q4f16_1

  4. Initialize the ChatModuleClient within your program with the desired parameters. Here's an example call:

    llama = dspy.ChatModuleClient(model='dist/prebuilt/mlc-chat-Llama-2-7b-chat-hf-q4f16_1', model_path='dist/prebuilt/lib/Llama-2-7b-chat-hf-q4f16_1-cuda.so')

Please refer to the official MLC repository for more detailed information and documentation.