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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 theCePO approach from Cerebras.

pip install optillmoptillm             2024-10-22 07:45:05,612 - INFO - Loaded plugin: privacy2024-10-22 07:45:06,293 - INFO - Loaded plugin: memory2024-10-22 07:45:06,293 - INFO - Starting server with approach: auto

docker pull ghcr.io/codelion/optillm:latestdocker run -p 8000:8000 ghcr.io/codelion/optillm:latest2024-10-22 07:45:05,612 - INFO - Loaded plugin: privacy2024-10-22 07:45:06,293 - INFO - Loaded plugin: memory2024-10-22 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

Clone the repository withgit and usepip install to setup the dependencies.

git clone https://github.com/codelion/optillm.gitcd optillmpython3 -m venv .venvsource .venv/bin/activatepip 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.

You can then run the optillm proxy as follows.

python optillm.py2024-09-06 07:57:14,191 - INFO - Starting server with approach: auto2024-09-06 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: off2024-09-06 07:57:14,212 - INFO - WARNING: This is a development server. Do not use itin 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://192.168.10.48:80002024-09-06 07:57:14,212 - INFO - Press CTRL+C to quit

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

importosfromopenaiimportOpenAIOPENAI_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 theOPENAI_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 usingmoa or mixture of agents as the optimization approach. In the proxy logs you will see the following showing themoa is been used with the base model asgpt-4o-mini.

2024-09-06 08:35:32,597 - INFO - Using approach moa, with gpt-4o-mini2024-09-06 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 theoptillm_approach field in theextra_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 yoursystem oruser 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 toauto. Otherwise, themodel attribute in the client request must be set with the model name only.

We now suport all LLM providers (by wrapping around theLiteLLM sdk). E.g. you can use the Gemini Flash model withmoa by setting passing the api key in the environment variableos.environ['GEMINI_API_KEY'] and then calling the modelmoa-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 - LiteLLMcompletion() model= gemini-1.5-flash-002; provider = gemini2024-09-29 19:43:21,011 - INFO - LiteLLMcompletion() model= gemini-1.5-flash-002; provider = gemini2024-09-29 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_handler2024-09-29 19:43:21,483 - INFO - Wrapper: Completed Call, calling success_handler19:43:21 - LiteLLM:INFO: utils.py:2952 - LiteLLMcompletion() model= gemini-1.5-flash-002; provider = gemini

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

In the diagram:

• A is an existing tool (likeoobabooga), framework (likepatchwork)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 thebase_url.
• C is any service providing an OpenAI API compatible chat completions endpoint.

We support loading any HuggingFace model or LoRA directly in optillm. To use the built-in inference server set theOPTILLM_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 theHF_TOKEN environment variablewith 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 modelmeta-llama/Llama-3.2-1B-Instruct and then adds two LoRAs on top -patched-codes/Llama-3.2-1B-FixVulns andpatched-codes/Llama-3.2-1B-FastApply.You can specify which LoRA to use using theactive_adapter param inextra_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 likecot_decoding andentropy_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,    })

• Set theOPENAI_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
• Runpython3 optillm.py --base_url base_url to start the proxy
  • e.g. for llama.cpp, runpython3 optillm.py --base_url http://localhost:8080/v1

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.

TheModel 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

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 inmcpServers 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

You can use any of theofficial 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"}

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

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)

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.

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.

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

optillm can optionally be built and run using Docker and the providedDockerfile.

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 athttp://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=mctsOPTILLM_MODEL=gpt-4

To secure the optillm proxy with an API key, set theOPTILLM_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 theAuthorization header:

Authorization: Bearer your_secret_api_key

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 withpatchwork which is an open-source framework that automates development gruntwork like PR reviews, bug fixing, security patching using workflowscalled patchflows. We saw huge performance gains across all the supported patchflows as shown below when using the mixture of agents approach (moa).

• CePO: Empowering Llama with Reasoning using Test-Time Compute -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

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}}