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. A good example of how to combine such techniques together is the CePO approach from Cerebras.

Installation

Using pip

pip install optillm
optillm
2024-10-22 07:45:05,612 - INFO - Loaded plugin: privacy
2024-10-22 07:45:06,293 - INFO - Loaded plugin: memory
2024-10-22 07:45:06,293 - INFO - Starting server with approach: auto

Using docker

docker pull ghcr.io/codelion/optillm:latest
docker run -p 8000:8000 ghcr.io/codelion/optillm:latest
2024-10-22 07:45:05,612 - INFO - Loaded plugin: privacy
2024-10-22 07:45:06,293 - INFO - Loaded plugin: memory
2024-10-22 07:45:06,293 - INFO - Starting server with approach: auto

To use optillm without local inference and only as a proxy you can add the -proxy suffix.

docker pull ghcr.io/codelion/optillm:latest-proxy

Install from source

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

We support all major LLM providers and models for inference. You need to set the correct environment variable and the proxy will pick the corresponding client.

Provider	Required Environment Variables	Additional Notes
OptiLLM	OPTILLM_API_KEY	Uses the inbuilt local server for inference, supports logprobs and decoding techniques like cot_decoding & entropy_decoding
OpenAI	OPENAI_API_KEY	You can use this with any OpenAI compatible endpoint (e.g. OpenRouter) by setting the base_url
Cerebras	CEREBRAS_API_KEY	You can use this for fast inference with supported models, see docs for details
Azure OpenAI	AZURE_OPENAI_API_KEY AZURE_API_VERSION AZURE_API_BASE	-
Azure OpenAI (Managed Identity)	AZURE_API_VERSION AZURE_API_BASE	Login required using az login, see docs for details
LiteLLM	depends on the model	See docs for details

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)

The code above applies to both OpenAI and Azure OpenAI, just remember to populate the OPENAI_API_KEY env variable with the proper key. There are multiple ways to control the optimization techniques, they are applied in the follow order of preference:

• 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 -

• Or, you can pass the slug in the optillm_approach field in the extra_body.

response = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[{ "role": "user","content": "" }],
  temperature=0.2,
  extra_body={"optillm_approach": "bon|moa|mcts"}
)

• Or, you can just mention the approach in either your system or user prompt, within <optillm_approach> </optillm_approach> tags.

response = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[{ "role": "user","content": "<optillm_approach>re2</optillm_approach> How many r's are there in strawberry?" }],
  temperature=0.2
)

Please note that the convention described above works only when the optillm server has been started with inference approach set to auto. Otherwise, the model attribute in the client request must be set with the model name only.

We now suport all LLM providers (by wrapping around the LiteLLM sdk). E.g. you can use the Gemini Flash model with moa by setting passing the api key in the environment variable os.environ['GEMINI_API_KEY'] and then calling the model moa-gemini/gemini-1.5-flash-002. In the output you will then see that LiteLLM is being used to call the base model.

9:43:21 - LiteLLM:INFO: utils.py:2952 -
LiteLLM completion() model= gemini-1.5-flash-002; provider = gemini
2024-09-29 19:43:21,011 - INFO -
LiteLLM completion() model= gemini-1.5-flash-002; provider = gemini
2024-09-29 19:43:21,481 - INFO - HTTP Request: POST https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-002:generateContent?key=[redacted] "HTTP/1.1 200 OK"
19:43:21 - LiteLLM:INFO: utils.py:988 - Wrapper: Completed Call, calling success_handler
2024-09-29 19:43:21,483 - INFO - Wrapper: Completed Call, calling success_handler
19:43:21 - LiteLLM:INFO: utils.py:2952 -
LiteLLM completion() model= gemini-1.5-flash-002; provider = gemini

The following sequence diagram illustrates how the request and responses go through optillm.

!Sequance diagram showing optillm in use

In the diagram:

• A is an existing tool (like oobabooga), framework (like patchwork) or your own code where you want to use the results from optillm. You can use it directly using any OpenAI client sdk.
• B is the optillm service (running directly or in a docker container) that will send requests to the base_url.
• C is any service providing an OpenAI API compatible chat completions endpoint.

Local inference server

We support loading any HuggingFace model or LoRA directly in optillm. To use the built-in inference server set the OPTILLM_API_KEY to any value (e.g. export OPTILLM_API_KEY="optillm") and then use the same in your OpenAI client. You can pass any HuggingFace model in model field. If it is a private model make sure you set the HF_TOKEN environment variable with your HuggingFace key. We also support adding any number of LoRAs on top of the model by using the + separator.

E.g. The following code loads the base model meta-llama/Llama-3.2-1B-Instruct and then adds two LoRAs on top - patched-codes/Llama-3.2-1B-FixVulns and patched-codes/Llama-3.2-1B-FastApply. You can specify which LoRA to use using the active_adapter param in extra_args field of OpenAI SDK client. By default we will load the last specified adapter.

OPENAI_BASE_URL = "http://localhost:8000/v1"
OPENAI_KEY = "optillm"
response = client.chat.completions.create(
  model="meta-llama/Llama-3.2-1B-Instruct+patched-codes/Llama-3.2-1B-FastApply+patched-codes/Llama-3.2-1B-FixVulns",
  messages=messages,
  temperature=0.2,
  logprobs = True,
  top_logprobs = 3,
  extra_body={"active_adapter": "patched-codes/Llama-3.2-1B-FastApply"},
)

You can also use the alternate decoding techniques like cot_decoding and entropy_decoding directly with the local inference server.

response = client.chat.completions.create(
  model="meta-llama/Llama-3.2-1B-Instruct",
  messages=messages,
  temperature=0.2,
  extra_body={
        "decoding": "cot_decoding",  # or "entropy_decoding"
        # CoT specific params
        "k": 10,
        "aggregate_paths": True,
        # OR Entropy specific params
        "top_k": 27,
        "min_p": 0.03,
    }
)

Starting the optillm proxy with an external server (e.g. llama.cpp or ollama)

• Set the OPENAI_API_KEY env variable to a placeholder value
  • e.g. export OPENAI_API_KEY="sk-no-key"
• Run ./llama-server -c 4096 -m path_to_model to start the server with the specified model and a context length of 4096 tokens
• Run python3 optillm.py --base_url base_url to start the proxy
  • e.g. for llama.cpp, run python3 optillm.py --base_url http://localhost:8080/v1

MCP Plugin

The Model Context Protocol (MCP) plugin enables OptiLLM to connect with MCP servers, bringing external tools, resources, and prompts into the context of language models. This allows for powerful integrations with filesystem access, database queries, API connections, and more.

What is MCP?

The Model Context Protocol (MCP) is an open protocol standard that allows LLMs to securely access tools and data sources through a standardized interface. MCP servers can provide:

• Tools: Callable functions that perform actions (like writing files, querying databases, etc.)
• Resources: Data sources for providing context (like file contents)
• Prompts: Reusable prompt templates for specific use cases

Configuration

Setting up MCP Config

1. Create a configuration file at ~/.optillm/mcp_config.json with the following structure:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/path/to/allowed/directory1",
        "/path/to/allowed/directory2"
      ],
      "env": {}
    }
  },
  "log_level": "INFO"
}

Each server entry in mcpServers consists of:

• Server name: A unique identifier for the server (e.g., "filesystem")
• command: The executable to run the server
• args: Command-line arguments for the server
• env: Environment variables for the server process
• description (optional): Description of the server's functionality

Available MCP Servers

You can use any of the official MCP servers or third-party servers. Some popular options include:

• Filesystem: @modelcontextprotocol/server-filesystem - File operations
• Git: mcp-server-git - Git repository operations
• SQLite: @modelcontextprotocol/server-sqlite - SQLite database access
• Brave Search: @modelcontextprotocol/server-brave-search - Web search capabilities

Example configuration for multiple servers:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/home/user/documents"],
      "env": {}
    },
    "search": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "your-api-key-here"
      }
    }
  },
  "log_level": "INFO"
}

Using the MCP Plugin

Once configured, the MCP plugin will automatically:

1. Connect to all configured MCP servers
2. Discover available tools, resources, and prompts
3. Make these capabilities available to the language model
4. Handle tool calls and resource requests

The plugin enhances the system prompt with MCP capabilities so the model knows which tools are available. When the model decides to use a tool, the plugin:

1. Executes the tool with the provided arguments
2. Returns the results to the model
3. Allows the model to incorporate the results into its response

Example Queries

Here are some examples of queries that will engage MCP tools:

• "List all the Python files in my documents directory" (Filesystem)
• "What are the recent commits in my Git repository?" (Git)
• "Search for the latest information about renewable energy" (Search)
• "Query my database for all users who registered this month" (Database)

Troubleshooting

Logs

The MCP plugin logs detailed information to:

~/.optillm/logs/mcp_plugin.log

Check this log file for connection issues, tool execution errors, and other diagnostic information.

Common Issues

1. Command not found: Make sure the server executable is available in your PATH, or use an absolute path in the configuration.

2. Connection failed: Verify the server is properly configured and any required API keys are provided.

3. Method not found: Some servers don't implement all MCP capabilities (tools, resources, prompts). Verify which capabilities the server supports.

4. Access denied: For filesystem operations, ensure the paths specified in the configuration are accessible to the process.

Implemented techniques

Approach	Slug	Description
Cerebras Planning and Optimization	cepo	Combines Best of N, Chain-of-Thought, Self-Reflection, Self-Improvement, and various prompting techniques
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
ReRead	re2	Implements rereading to improve reasoning by processing queries twice
Self-Consistency	self_consistency	Implements an advanced self-consistency method
Z3 Solver	z3	Utilizes the Z3 theorem prover for logical reasoning
R* Algorithm	rstar	Implements the R* algorithm for problem-solving
LEAP	leap	Learns task-specific principles from few shot examples
Round Trip Optimization	rto	Optimizes responses through a round-trip process
Best of N Sampling	bon	Generates multiple responses and selects the best one
Mixture of Agents	moa	Combines responses from multiple critiques
Monte Carlo Tree Search	mcts	Uses MCTS for decision-making in chat responses
PV Game	pvg	Applies a prover-verifier game approach at inference time
CoT Decoding	N/A for proxy	Implements chain-of-thought decoding to elicit reasoning without explicit prompting
Entropy Decoding	N/A for proxy	Implements adaptive sampling based on the uncertainty of tokens during generation
Thinkdeeper	N/A for proxy	Implements the reasoning_effort param from OpenAI for reasoning models like DeepSeek R1
AutoThink	N/A for proxy	Combines query complexity classification with steering vectors to enhance reasoning

Implemented plugins

Plugin	Slug	Description
System Prompt Learning	spl	Implements what Andrej Karpathy called the third paradigm for LLM learning, this enables the model to acquire program solving knowledge and strategies
Deep Think	deepthink	Implements a Gemini-like Deep Think approach using inference time scaling for reasoning LLMs
Long-Context Cerebras Planning and Optimization	longcepo	Combines planning and divide-and-conquer processing of long documents to enable infinite context
MCP Client	mcp	Implements the model context protocol (MCP) client, enabling you to use any LLM with any MCP Server
Router	router	Uses the optillm-modernbert-large model to route requests to different approaches based on the user prompt
Chain-of-Code	coc	Implements a chain of code approach that combines CoT with code execution and LLM based code simulation
Memory	memory	Implements a short term memory layer, enables you to use unbounded context length with any LLM
Privacy	privacy	Anonymize PII data in request and deanonymize it back to original value in response
Read URLs	readurls	Reads all URLs found in the request, fetches the content at the URL and adds it to the context
Execute Code	executecode	Enables use of code interpreter to execute python code in requests and LLM generated responses
JSON	json	Enables structured outputs using the outlines library, supports pydantic types and JSON schema

Available parameters

optillm supports various command-line arguments for configuration. When using Docker, these can also be set as environment variables prefixed with OPTILLM_.

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
--optillm-api-key	Optional API key for client authentication to optillm	""
--cepo_*	See CePO Parameters section below for detailed config options	Various

CePO Parameters

Parameter	Description	Default Value
--cepo_bestofn_n	Number of responses to be generated in best of n stage	3
--cepo_bestofn_temperature	Temperature for verifier in best of n stage	0.1
--cepo_bestofn_max_tokens	Maximum number of tokens for verifier in best of n stage	4096
--cepo_bestofn_rating_type	Type of rating in best of n stage ("absolute" or "pairwise")	"absolute"
--cepo_planning_n	Number of plans generated in planning stage	3
--cepo_planning_m	Number of attempts to generate n plans in planning stage	6
--cepo_planning_temperature_step1	Temperature for generator in step 1 of planning stage	0.55
--cepo_planning_temperature_step2	Temperature for generator in step 2 of planning stage	0.25
--cepo_planning_temperature_step3	Temperature for generator in step 3 of planning stage	0.1
--cepo_planning_temperature_step4	Temperature for generator in step 4 of planning stage	0
--cepo_planning_max_tokens_step1	Maximum number of tokens in step 1 of planning stage	4096
--cepo_planning_max_tokens_step2	Maximum number of tokens in step 2 of planning stage	4096
--cepo_planning_max_tokens_step3	Maximum number of tokens in step 3 of planning stage	4096
--cepo_planning_max_tokens_step4	Maximum number of tokens in step 4 of planning stage	4096
--cepo_print_output	Whether to print the output of each stage	False
--cepo_config_file	Path to CePO configuration file	None
--cepo_use_plan_diversity	Use additional plan diversity step	False
--cepo_rating_model	Specify a model for rating step if different than for completion	None

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

SOTA results on benchmarks with optillm

AutoThink on GPQA-Diamond & MMLU-Pro (May 2025)

Model	GPQA-Diamond		MMLU-Pro
	Accuracy (%)	Avg. Tokens	Accuracy (%)	Avg. Tokens
DeepSeek-R1-Distill-Qwen-1.5B	21.72	7868.26	25.58	2842.75
with Fixed Budget	28.47	3570.00	26.18	1815.67
with AutoThink	31.06	3520.52	26.38	1792.50

LongCePO on LongBench v2 (Apr 2025)

Model¹	Context window	Short samples (up to 32K words)	Medium samples (32–128K words)
Llama 3.3 70B Instruct	128K	36.7 (45.0)	27.0 (33.0)
LongCePO + Llama 3.3 70B Instruct	8K	36.8 ± 1.38	38.7 ± 2.574 (39.735)²
Mistral-Large-Instruct-2411	128K	41.7 (46.1)	30.7 (34.9)
o1-mini-2024-09-12	128K	48.6 (48.9)	33.3 (32.9)
Claude-3.5-Sonnet-20241022	200K	46.1 (53.9)	38.6 (41.9)
Llama-4-Maverick-17B-128E-Instruct	524K	32.22 (50.56)	28.84 (41.86)

¹ Performance numbers reported by LongBench v2 authors, except for LongCePO and Llama-4-Maverick results.

² Numbers in parentheses for LongCePO indicate accuracy of majority voting from 5 runs.

LongCePO on HELMET - InfiniteBench En.MC, 128K length (Apr 2025)

Model	Accuracy (%)
Llama 3.3 70B Instruct (full context)	58.0
LongCePO + Llama 3.3 70B Instruct (8K context)	71.6 ± 1.855 (73.0)¹
o1-mini-2024-09-12 (full context)	58.0
gpt-4o-2024-08-06 (full context)	74.0

¹ Numbers in parentheses for LongCePO indicate accuracy of majority voting from 5 runs.

CePO on math and code benchmarks (Mar 2025)

Method	Math-L5	MMLU-Pro (Math)	CRUX	LiveCodeBench (pass@1)	Simple QA
Llama 3.3 70B	51.0	78.6	72.6	27.1	20.9
Llama 3.1 405B	49.8	79.2	73.0	31.8	13.5
CePO (using Llama 3.3 70B)	69.6	84.8	80.1	31.9	22.6
QwQ 32B	61.4	90.8	82.5	44.3	7.8
CePO (using QwQ 32B)	88.1	92.0	86.3	51.5	8.2
DeepSeek R1 Llama	83.1	82.0	84.0	47.3	14.6
CePO (using DeepSeek R1 Llama)	90.2	84.0	89.4	47.2	15.5

coc-claude-3-5-sonnet-20241022 on AIME 2024 pass@1 (Nov 2024)

Model	Score
o1-mini	56.67
coc-claude-3-5-sonnet-20241022	46.67
coc-gemini/gemini-exp-1121	46.67
o1-preview	40.00
gemini-exp-1114	36.67
claude-3-5-sonnet-20241022	20.00
gemini-1.5-pro-002	20.00
gemini-1.5-flash-002	16.67

readurls&memory-gpt-4o-mini on Google FRAMES Benchmark (Oct 2024)

Model	Accuracy
readurls&memory-gpt-4o-mini	61.29
gpt-4o-mini	50.61
readurls&memory-Gemma2-9b	30.1
Gemma2-9b	5.1
Gemma2-27b	30.8
Gemini Flash 1.5	66.5
Gemini Pro 1.5	72.9

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)

!Results showing Mixture of Agents approach using gpt-4o-mini on Arena Hard Auto Benchmark

optillm with Patchwork (July 2024)

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 mixture of agents approach (moa).

!Results showing optillm mixture of agents approach used with patchflows

References

• AutoThink: efficient inference for reasoning LLMs - Implementation
• Self-Discover: Large Language Models Self-Compose Reasoning Structures
• Implementation
• CePO: Empowering Llama with Reasoning using Test-Time Compute - Implementation
• LongCePO: Empowering LLMs to efficiently leverage infinite context - Implementation
• Chain of Code: Reasoning with a Language Model-Augmented Code Emulator - Inspired the implementation of coc plugin
• Entropy Based Sampling and Parallel CoT Decoding - Implementation
• Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation - Evaluation script
• Writing in the Margins: Better Inference Pattern for Long Context Retrieval - Inspired the implementation of the memory plugin
• Chain-of-Thought Reasoning Without Prompting - Implementation
• Re-Reading Improves Reasoning in Large Language Models - Implementation
• In-Context Principle Learning from Mistakes - Implementation
• Planning In Natural Language Improves LLM Search For Code Generation - Implementation
• Self-Consistency Improves Chain of Thought Reasoning in Language Models - Implementation
• Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers - Implementation
• Mixture-of-Agents Enhances Large Language Model Capabilities - Inspired the implementation of moa
• Prover-Verifier Games improve legibility of LLM outputs - Implementation
• Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning - Inspired the implementation of mcts
• Unsupervised Evaluation of Code LLMs with Round-Trip Correctness - Inspired the implementation of rto
• Patched MOA: optimizing inference for diverse software development tasks - Implementation
• Patched RTC: evaluating LLMs for diverse software development tasks - Implementation

Citation

If you use this library in your research, please cite:

@software{optillm,
  title = {Optillm: Optimizing inference proxy for LLMs},
  author = {Asankhaya Sharma},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/codelion/optillm}
}