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README.md
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README.md
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| Approach | Slug | Description |
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| ------------------------------------ | ------------------ | ---------------------------------------------------------------------------------------------- |
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| Cerebras Planning and Optimization | `cepo` | Combines Best of N, Chain-of-Thought, Self-Reflection, Self-Improvement, and various prompting techniques |
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| [Cerebras Planning and Optimization](optillm/cepo) | `cepo` | Combines Best of N, Chain-of-Thought, Self-Reflection, Self-Improvement, and various prompting techniques |
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| CoT with Reflection | `cot_reflection` | Implements chain-of-thought reasoning with \<thinking\>, \<reflection> and \<output\> sections |
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| PlanSearch | `plansearch` | Implements a search algorithm over candidate plans for solving a problem in natural language |
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| ReRead | `re2` | Implements rereading to improve reasoning by processing queries twice |
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| CoT Decoding | N/A for proxy | Implements chain-of-thought decoding to elicit reasoning without explicit prompting |
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| Entropy Decoding | N/A for proxy | Implements adaptive sampling based on the uncertainty of tokens during generation |
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| Thinkdeeper | N/A for proxy | Implements the `reasoning_effort` param from OpenAI for reasoning models like DeepSeek R1 |
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| AutoThink | N/A for proxy | Combines query complexity classification with steering vectors to enhance reasoning |
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| [AutoThink](optillm/autothink) | N/A for proxy | Combines query complexity classification with steering vectors to enhance reasoning |
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## Implemented plugins
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| Plugin | Slug | Description |
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| ----------------------- | ------------------ | ---------------------------------------------------------------------------------------------- |
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| System Prompt Learning | `spl` | Implements what [Andrej Karpathy called the third paradigm](https://x.com/karpathy/status/1921368644069765486) for LLM learning, this enables the model to acquire program solving knowledge and strategies |
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| Deep Think | `deepthink` | Implements a Gemini-like Deep Think approach using inference time scaling for reasoning LLMs |
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| Long-Context Cerebras Planning and Optimization | `longcepo` | Combines planning and divide-and-conquer processing of long documents to enable infinite context |
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| [System Prompt Learning](optillm/plugins/spl) | `spl` | Implements what [Andrej Karpathy called the third paradigm](https://x.com/karpathy/status/1921368644069765486) for LLM learning, this enables the model to acquire program solving knowledge and strategies |
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| [Deep Think](optillm/plugins/deepthink) | `deepthink` | Implements a Gemini-like Deep Think approach using inference time scaling for reasoning LLMs |
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| [Long-Context Cerebras Planning and Optimization](optillm/plugins/longcepo) | `longcepo` | Combines planning and divide-and-conquer processing of long documents to enable infinite context |
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| MCP Client | `mcp` | Implements the model context protocol (MCP) client, enabling you to use any LLM with any MCP Server |
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| Router | `router` | Uses the [optillm-modernbert-large](https://huggingface.co/codelion/optillm-modernbert-large) model to route requests to different approaches based on the user prompt |
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| Chain-of-Code | `coc` | Implements a chain of code approach that combines CoT with code execution and LLM based code simulation |
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