Quantization made by Richard Erkhov.
OpenCerebrum-1.0-7b-DPO - GGUF
Original model description:
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:
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- 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:
- 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:
- 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:
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
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
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.
Detailed results can be found
here| 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 |
