py-txi 0.7.0

A Python wrapper around TGI and TEI servers

Py-TXI (previously Py-TGI)

Py-TXI is a Python wrapper around Text-Generation-Inference and Text-Embedding-Inference that enables creating and running TGI/TEI instances through the awesome docker-py in a similar style to Transformers API.

Installation

pip install py-txi

Py-TXI is designed to be used in a similar way to Transformers API. We use docker-py (instead of a dirty subprocess solution) so that the containers you run are linked to the main process and are stopped automatically when your code finishes or fails.

Advantages

• Easy to use: Py-TXI is designed to be used in a similar way to Transformers API.
• Automatic cleanup: Py-TXI stops the Docker container when your code finishes or fails.
• Batched inference: Py-TXI supports sending a batch of inputs to the server for inference.
• Automatic port allocation: Py-TXI automatically allocates a free port for the Inference server.
• Configurable: Py-TXI allows you to configure the Inference servers using a simple configuration object.
• Verbose: Py-TXI streams the logs of the underlying Docker container to the main process so you can debug easily.

Usage

Here's an example of how to use it:

from py_txi import TGI, TGIConfig

llm = TGI(config=TGIConfig(model_id="bigscience/bloom-560m", gpus="0"))
output = llm.generate(["Hi, I'm a language model", "I'm fine, how are you?"])
print("LLM:", output)
llm.close()

Output: LLM: [' student. I have a problem with the following code. I have a class that has a method that', '"\n\n"I\'m fine," said the girl, "but I don\'t want to be alone.']

from py_txi import TEI, TEIConfig

embed = TEI(config=TEIConfig(model_id="BAAI/bge-base-en-v1.5"))
output = embed.encode(["Hi, I'm an embedding model", "I'm fine, how are you?"])
print("Embed:", output)
embed.close()

Output: [array([[ 0.01058742, -0.01588806, -0.03487622, ..., -0.01613717, 0.01772875, -0.02237891]], dtype=float32), array([[ 0.02815401, -0.02892136, -0.0536355 , ..., 0.01225784, -0.00241452, -0.02836569]], dtype=float32)]

That's it! Now you can write your Python scripts using the power of TGI and TEI without having to worry about the underlying Docker containers.