---
language:
- en
license: apache-2.0
tags:
- open-source
- code
- math
- chemistry
- biology
- text-generation
- question-answering
datasets:
- Locutusque/OpenCerebrum-dpo
pipeline_tag: text-generation
model-index:
- name: OpenCerebrum-1.0-7b-DPO
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 62.71
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 84.33
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 62.59
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 44.91
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 80.11
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 42.0
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/OpenCerebrum-1.0-7b-DPO
      name: Open LLM Leaderboard
---

# OpenCerebrum-1.0-7B-DPO

OpenCerebrum-1.0-7B-DPO is an open-source language model fine-tuned from the alpindale/Mistral-7B-v0.2-hf base model on a diverse dataset aimed at replicating capabilities of Aether Research's proprietary Cerebrum model. 

The model was fine-tuned on approximately 21,000 examples across 6 datasets spanning coding, math, science, reasoning, and general instruction-following. The goal was to assemble public datasets that could help the model achieve strong performance on benchmarks where Cerebrum excels.

I used the ChatML prompt format to train this model. 

## Model Details

- **Base Model:** alpindale/Mistral-7B-v0.2-hf
- **Parameters:** 7 billion 
- **Fine-Tuning Dataset Size:** ~21,000 examples
- **Fine-Tuning Data:** Amalgamation of 6 public datasets
- **Language:** English
- **License:** Apache 2.0

## Quants

- **ExLlamaV2:** https://huggingface.co/bartowski/OpenCerebrum-1.0-7b-DPO-exl2
- **GGUF:** https://huggingface.co/bartowski/OpenCerebrum-1.0-7b-DPO-GGUF
- **AWQ:** https://huggingface.co/solidrust/OpenCerebrum-1.0-7b-DPO-AWQ

## Intended Use

OpenCerebrum-1.0-7B-DPO is intended to be a powerful open-source model for coding, math, science, and general question-answering and text generation tasks. Its diverse fine-tuning data aims to equip it with broad knowledge and reasoning capabilities.

However, as an open-source replica trained on a subset of data compared to the original Cerebrum, it may not match Cerebrum's full performance. Additionally, biases and limitations of the fine-tuning data may be reflected in the model's outputs.

## Limitations and Biases

- The model may have biases and limitations inherited from its fine-tuning datasets. Thorough testing is needed to characterize these.
- With 21,000 training examples, the fine-tuning data is still limited compared to the proprietary Cerebrum data.
- As the model is based on a 7B parameter model, it has computational and memory constraints compared to larger models.

## Training Details

The model was fine-tuned on the 6 datasets listed in the Datasets section, totaling approximately 21,000 examples. In the future, the fine-tuning dataset may be condensed to more closely match the ~500 example dataset reputedly used for the original Cerebrum model.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__OpenCerebrum-1.0-7b-DPO)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |62.78|
|AI2 Reasoning Challenge (25-Shot)|62.71|
|HellaSwag (10-Shot)              |84.33|
|MMLU (5-Shot)                    |62.59|
|TruthfulQA (0-shot)              |44.91|
|Winogrande (5-shot)              |80.11|
|GSM8k (5-shot)                   |42.00|