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README.md
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---
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language:
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- en
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tags:
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- problem-solving
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- chain-of-thought
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- reasoning
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base_model:
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- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
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- Skywork/Skywork-o1-Open-Llama-3.1-8B
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- NousResearch/DeepHermes-3-Llama-3-8B-Preview
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- O1-OPEN/OpenO1-LLama-8B-v0.1
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- SimpleBerry/LLaMA-O1-Supervised-1129
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- terrycraddock/Reflection-Llama-3.1-8B
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---
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# Llama3.1-deep-o1
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This is a merge of several DeepSeek R1 distilled and O1-style long chain-of-thought (CoT) large language models (LLMs). It is designed for generating long, coherent solutions and excels at problem-solving tasks among models with 8 billion parameters.
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## Model Overview
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**Key Features:**
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- Generates detailed, manual-like explanations for complex questions.
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- Suitable for creating solution outlines, analyzing problems, and writing essays.
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**Limitations:**
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- Does not follow standard CoT formats like `<thought>` tags.
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- Prone to calculation errors and careless mistakes in reasoning.
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- Struggles with multiturn conversations and user alignment.
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- For example, it may ask questions mid-response but continue answering regardless, in line with its CoT origins.
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## Merge Details
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The model was created using the following YAML configuration:
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```yaml
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models:
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- model: DeepSeek-R1-Distill-Llama-8B
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parameters:
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weight: 1.5
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- model: Skywork-o1-Open-Llama-3.1-8B
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parameters:
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weight: 1.5
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- model: DeepHermes-3-Llama-3-8B-Preview
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- model: OpenO1-LLama-8B-v0.1
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- model: LLaMA-O1-Supervised-1129
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- model: Reflection-Llama-3.1-8B
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merge_method: linear
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parameters:
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weight: 1
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dtype: bfloat16
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```
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## Usage Recommendations
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- To generate hints for scientific problem solving
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- As a foundation model for finetuning and merging
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### Examples to Try:
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- Write the equations for glycolysis and pyruvate oxidation.
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- Calculate net ATP formation from glucose metabolism (excluding electron transport chain).
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- Integrate x^2 e^x dx.
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- Prove that the complete bipartite graph K_{3,3} isn't planar.
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- Derive a formula for the critical angle between two media with refractive indices n_1 and n_2.
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- Compare steam vs. diesel engines including their capabilities and historical significance.
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### Notes on Performance:
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While the model provides coherent and expert-like responses, users should verify its outputs for accuracy - especially in calculations or logical reasoning tasks.
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## Warning
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- This model is experimental and may require careful validation when used for critical applications.
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- It is not optimized for conversational tasks but performs well in single-turn question answering.
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