optillm

optillm is an OpenAI API compatible optimizing inference proxy which implements several state-of-the-art techniques that can improve the accuracy and performance of LLMs. The current focus is on implementing techniques that improve reasoning over coding, logical and mathematical queries. It is possible to beat the frontier models using these techniques across diverse tasks by doing additional compute at inference time.

SOTA results with optillm

plansearch-gpt-4o-mini on LiveCodeBench (Sep 2024)

Model	pass@1	pass@5	pass@10
plansearch-gpt-4o-mini	44.03	59.31	63.5
gpt-4o-mini	43.9	50.61	53.25
claude-3.5-sonnet	51.3
gpt-4o-2024-05-13	45.2
gpt-4-turbo-2024-04-09	44.2

moa-gpt-4o-mini on Arena-Hard-Auto (Aug 2024)

Installation

Just clone the repository with git and use pip install to setup the dependencies.

git clone https://github.com/codelion/optillm.git
cd optillm
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

You can then run the optillm proxy as follows.

python optillm.py                           
2024-09-06 07:57:14,191 - INFO - Starting server with approach: auto
2024-09-06 07:57:14,191 - INFO - Server configuration: {'approach': 'auto', 'mcts_simulations': 2, 'mcts_exploration': 0.2, 'mcts_depth': 1, 'best_of_n': 3, 'model': 'gpt-4o-mini', 'rstar_max_depth': 3, 'rstar_num_rollouts': 5, 'rstar_c': 1.4, 'base_url': ''}
 * Serving Flask app 'optillm'
 * Debug mode: off
2024-09-06 07:57:14,212 - INFO - WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
 * Running on all addresses (0.0.0.0)
 * Running on http://127.0.0.1:8000
 * Running on http://192.168.10.48:8000
2024-09-06 07:57:14,212 - INFO - Press CTRL+C to quit

Usage

Once the proxy is running, you can just use it as a drop in replacement for an OpenAI client by setting the base_url as http://localhost:8000/v1.

import os
from openai import OpenAI

OPENAI_KEY = os.environ.get("OPENAI_API_KEY")
OPENAI_BASE_URL = "http://localhost:8000/v1"
client = OpenAI(api_key=OPENAI_KEY, base_url=OPENAI_BASE_URL)

response = client.chat.completions.create(
  model="moa-gpt-4o",
  messages=[
    {
      "role": "user",
      "content": "Write a Python program to build an RL model to recite text from any position that the user provides, using only numpy."
    }
  ],
  temperature=0.2
)

print(response)

You can control the technique you use for optimization by prepending the slug to the model name {slug}-model-name. E.g. in the above code we are using moa or mixture of agents as the optimization approach. In the proxy logs you will see the following showing the moa is been used with the base model as gpt-4o-mini.

2024-09-06 08:35:32,597 - INFO - Using approach moa, with gpt-4o-mini
2024-09-06 08:35:35,358 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2024-09-06 08:35:39,553 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2024-09-06 08:35:44,795 - INFO - HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
2024-09-06 08:35:44,797 - INFO - 127.0.0.1 - - [06/Sep/2024 08:35:44] "POST /v1/chat/completions HTTP/1.1" 200 -

Implemented techniques

Technique	Slug	Description
Monte Carlo Tree Search	`mcts`	Uses MCTS for decision-making in chat responses
Best of N Sampling	`bon`	Generates multiple responses and selects the best one
Mixture of Agents	`moa`	Combines responses from multiple critiques
Round Trip Optimization	`rto`	Optimizes responses through a round-trip process
Z3 Solver	`z3`	Utilizes the Z3 theorem prover for logical reasoning
Self-Consistency	`self_consistency`	Implements an advanced self-consistency method
PV Game	`pvg`	Applies a prover-verifier game approach at inference time
R* Algorithm	`rstar`	Implements the R* algorithm for problem-solving
CoT with Reflection	`cot_reflection`	Implements chain-of-thought reasoning with <thinking>, <reflection> and <output> sections
PlanSearch	`plansearch`	Implements a search algorithm over candidate plans for solving a problem in natural language

DanozWorld/codelion_optillm