agentlans / Llama3.1-deep-o1

Llama3.1-deep-o1

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.

Model Overview

Key Features:

• Generates detailed, manual-like explanations for complex questions.
• Suitable for creating solution outlines, analyzing problems, and writing essays.

Limitations:

• Does not follow standard CoT formats like <thought> tags.
• Prone to calculation errors and careless mistakes in reasoning.
• Struggles with multiturn conversations and user alignment.
• For example, it may ask questions mid-response but continue answering regardless, in line with its CoT origins.

Merge Details

The model was created using the following YAML configuration:

models:
  - model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
    parameters:
      weight: 1.5
  - model: Skywork/Skywork-o1-Open-Llama-3.1-8B
    parameters:
      weight: 1.5
  - model: NousResearch/DeepHermes-3-Llama-3-8B-Preview
  - model: O1-OPEN/OpenO1-LLama-8B-v0.1
  - model: SimpleBerry/LLaMA-O1-Supervised-1129
  - model: terrycraddock/Reflection-Llama-3.1-8B
merge_method: linear
parameters:
  weight: 1
dtype: bfloat16

Usage Recommendations

• To generate hints for scientific problem solving
• As a foundation model for finetuning and merging

Examples to Try:

• Write the equations for glycolysis and pyruvate oxidation.
• Calculate net ATP formation from glucose metabolism (excluding electron transport chain).
• Integrate x^2 e^x dx.
• Prove that the complete bipartite graph K_{3,3} isn't planar.
• Derive a formula for the critical angle between two media with refractive indices n_1 and n_2.
• Compare steam vs. diesel engines including their capabilities and historical significance.

Notes on Performance:

While the model provides coherent and expert-like responses, users should verify its outputs for accuracy - especially in calculations or logical reasoning tasks.

Warning

• This model is experimental and may require careful validation when used for critical applications.
• It is not optimized for conversational tasks but performs well in single-turn question answering.