codelion/optillm - Automatic prompt strategy proxy

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• [ ] optillm/README.md at main · codelion/optillm

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 moa-gpt-4o-mini on Arena-Hard-Auto

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>, \ and \<output> sections
PlanSearch	plansearch	Implements a search algorithm over candidate plans for solving a problem in natural language

References

• 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

Suggested labels

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