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.

Patchwork with optillm

Since optillm is a drop-in replacement for OpenAI API you can easily integrate it with existing tools and frameworks using the OpenAI client. We used optillm with patchwork which is an open-source framework that automates development gruntwork like PR reviews, bug fixing, security patching using workflows called patchflows. We saw huge performance gains across all the supported patchflows as shown below when using the mixutre of agents approach (moa).

SOTA results on benchmarks 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 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
Agent	agent	Determines which of the below approaches to take and then combines the results
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
LEAP	leap	Learns task-specific principles from few shot examples

Available Parameters

optillm supports various command-line arguments and environment variables for configuration.

Parameter	Description	Default Value
--approach	Inference approach to use	"auto"
--simulations	Number of MCTS simulations	2
--exploration	Exploration weight for MCTS	0.2
--depth	Simulation depth for MCTS	1
--best-of-n	Number of samples for best_of_n approach	3
--model	OpenAI model to use	"gpt-4o-mini"
--base-url	Base URL for OpenAI compatible endpoint	""
--rstar-max-depth	Maximum depth for rStar algorithm	3
--rstar-num-rollouts	Number of rollouts for rStar algorithm	5
--rstar-c	Exploration constant for rStar algorithm	1.4
--n	Number of final responses to be returned	1
--return-full-response	Return the full response including the CoT with tags	False
--port	Specify the port to run the proxy	8000
--api-key	Optional API key for client authentication to optillm	""

When using Docker, these can be set as environment variables prefixed with OPTILLM_.

Running with Docker

optillm can optionally be built and run using Docker and the provided Dockerfile.

Using Docker Compose

1. Make sure you have Docker and Docker Compose installed on your system.

2. Either update the environment variables in the docker-compose.yaml file or create a .env file in the project root directory and add any environment variables you want to set. For example, to set the OpenAI API key, add the following line to the .env file:

   OPENAI_API_KEY=your_openai_api_key_here

3. Run the following command to start optillm:

   docker compose up -d

   This will build the Docker image if it doesn't exist and start the optillm service.

4. optillm will be available at http://localhost:8000.

When using Docker, you can set these parameters as environment variables. For example, to set the approach and model, you would use:

OPTILLM_APPROACH=mcts
OPTILLM_MODEL=gpt-4

To secure the optillm proxy with an API key, set the OPTILLM_API_KEY environment variable:

OPTILLM_API_KEY=your_secret_api_key

When the API key is set, clients must include it in their requests using the Authorization header:

Authorization: Bearer your_secret_api_key

References

• In-Context Principle Learning from Mistakes
• Planning In Natural Language Improves LLM Search For Code Generation
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
• Mixture-of-Agents Enhances Large Language Model Capabilities
• Prover-Verifier Games improve legibility of LLM outputs
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
• Unsupervised Evaluation of Code LLMs with Round-Trip Correctness
• Patched MOA: optimizing inference for diverse software development tasks
• Patched RTC: evaluating LLMs for diverse software development tasks