README.md

Serving a fine tuned LLaMA model with HuggingFace text-generation-inference server

This document shows how to serve a fine tuned LLaMA mode with HuggingFace's text-generation-inference server. This option is currently only available for models that were trained using the LoRA method or without using the --use_peft argument.

Step 0: Merging the weights (Only required if LoRA method was used)

In case the model was fine tuned with LoRA method we need to merge the weights of the base model with the adapter weight. For this we can use the script merge_lora_weights.py which is located in the same folder as this README file.

The script takes the base model, the peft weight folder as well as an output as arguments:

python inference/hf-text-generation-inference/merge_lora_weights.py --base_model llama-7B --peft_model ft_output --output_dir data/merged_model_output

Step 1: Serving the model

Subsequently, the model can be served using the docker container provided by hf text-generation-inference started from the main directory of this repository:

model=/data/merged_model_output
num_shard=2
volume=$PWD/inference/hf-text-generation-inference/data
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --num-shard $num_shard

The num_shard argument determines the number of GPU's the model should be sharded on.

Step 2: Running inference

After the loading of the model shards completed an inference can be executed by using one of the following commands:

curl 127.0.0.1:8080/generate \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17}}' \
    -H 'Content-Type: application/json'
# OR for streaming inference
curl 127.0.0.1:8080/generate_stream \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17}}' \
    -H 'Content-Type: application/json'

Further information can be found in the documentation of the hf text-generation-inference solution.