llama-index-llms-pipeshift 0.2.0

llama-index llms pipeshift integration

LlamaIndex Llms Integration: Pipeshift

Pipeshift provides a fast and scalable infrastructure for fine-tuning and inferencing open-source LLMs. We abstract away the training + inferencing infrastructure and the tooling around it, enabling engineering teams to get to production with all the optimizations and one-click deployments.

Installation

1. Install the required Python packages:

   %pip install llama-index-llms-pipeshift
   %pip install llama-index
   

2. Set the PIPESHIFT_API_KEY as an environment variable or pass it directly to the class constructor.

3. Choose any of the pre-deployed models or the one deployed by you from deployments section of pipeshift dashboard

Usage

Basic Completion

To generate a simple completion, use the complete method:

from llama_index.llms.pipeshift import Pipeshift

llm = Pipeshift(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    # api_key="YOUR_API_KEY" # alternative way to pass api_key if not specified in environment variable
)
res = llm.complete("supercars are ")
print(res)

Example output:

Supercars are high-performance sports cars that are designed to deliver exceptional speed, power, and luxury. They are often characterized by their sleek and aerodynamic designs, powerful engines, and advanced technology.

Basic Chat

To simulate a chat with multiple messages:

from llama_index.core.llms import ChatMessage
from llama_index.llms.pipeshift import Pipeshift

messages = [
    ChatMessage(
        role="system", content="You are sales person at supercar showroom"
    ),
    ChatMessage(role="user", content="why should I pick porsche 911 gt3 rs"),
]
res = Pipeshift(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct", max_tokens=50
).chat(messages)
print(res)

Example output:

assistant: 1. Unmatched Performance: The Porsche 911 GT3 RS is a high-performance sports car that delivers an unparalleled driving experience. It boasts a powerful 4.0-liter flat

Streaming Completion

To stream a response in real-time using stream_complete:

from llama_index.llms.pipeshift import Pipeshift

llm = Pipeshift(model="meta-llama/Meta-Llama-3.1-8B-Instruct")
resp = llm.stream_complete("porsche GT3 RS is ")

for r in resp:
    print(r.delta, end="")

Example output (partial):

 The Porsche 911 GT3 RS is a high-performance sports car produced by Porsche AG. It is part of the 911 (991 and 992 generations) series and is%

Streaming Chat

For a streamed conversation, use stream_chat:

from llama_index.llms.pipeshift import Pipeshift
from llama_index.core.llms import ChatMessage

llm = Pipeshift(model="meta-llama/Meta-Llama-3.1-8B-Instruct")
messages = [
    ChatMessage(
        role="system", content="You are sales person at supercar showroom"
    ),
    ChatMessage(role="user", content="how fast can porsche gt3 rs it go?"),
]
resp = llm.stream_chat(messages)

for r in resp:
    print(r.delta, end="")

Example output (partial):

The Porsche 911 GT3 RS is an incredible piece of engineering. This high-performance sports car can reach a top speed of approximately 193 mph (310 km/h) according to P%

LLM Implementation example

Examples