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---
language:
- en
license: apache-2.0
tags:
- open-source
- code
- math
- chemistry
- biology
- text-generation
- question-answering
pipeline_tag: text-generation
---
# OpenCerebrum-2.0-7B
OpenCerebrum-2.0-7B 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 with SFT and DPO on approximately 7,000 examples across 15 data sources spanning coding, math, science, multi-turn conversation, RAG, 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.
## Model Details
- **Base Model:** alpindale/Mistral-7B-v0.2-hf
- **Parameters:** 7 billion
- **Fine-Tuning Dataset Size:** ~7,000 examples
- **Fine-Tuning Data:** Advanced in-house curation techniques at Cognitive Computations, with 15 different data sources for DPO and SFT.
- **Language:** English
- **License:** Apache 2.0
## Quants
### EXL2 [@bartowski](https://huggingface.co/bartowski/)
- https://huggingface.co/bartowski/OpenCerebrum-2.0-7B-exl2
### GGUF [@bartowski](https://huggingface.co/bartowski/)
- https://huggingface.co/bartowski/OpenCerebrum-2.0-7B-GGUF
## Intended Use
OpenCerebrum-2.0-7B 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.
- As the model is based on a 7B parameter model, it has computational and memory constraints compared to larger models.
## Evaluations
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|--------------|------:|------|-----:|------|-----:|---|-----:|
|truthfulqa_mc2| 2|none | 0|acc |0.5182|± |0.0152|
|ai2_arc |N/A |none | 0|acc |0.7060|± |0.0073|
| | |none | 0|acc_norm|0.7049|± |0.0074|
| - arc_challenge | 1|none | 0|acc |0.5000|± |0.0146|
| | |none | 0|acc_norm|0.5299|± |0.0146|
| - arc_easy | 1|none | 0|acc |0.8077|± |0.0081|
| | |none | 0|acc_norm|0.7912|± |0.0083|
|agieval_nous |N/A |none | 0|acc |0.3778|± |0.0093|
| | |none | 0|acc_norm|0.3574|± |0.0093|
| - agieval_aqua_rat | 1|none | 0|acc |0.2402|± |0.0269|
| | |none | 0|acc_norm|0.2205|± |0.0261|
| - agieval_logiqa_en | 1|none | 0|acc |0.3164|± |0.0182|
| | |none | 0|acc_norm|0.3656|± |0.0189|
| - agieval_lsat_ar | 1|none | 0|acc |0.2130|± |0.0271|
| | |none | 0|acc_norm|0.1913|± |0.0260|
| - agieval_lsat_lr | 1|none | 0|acc |0.4078|± |0.0218|
| | |none | 0|acc_norm|0.3647|± |0.0213|
| - agieval_lsat_rc | 1|none | 0|acc |0.4981|± |0.0305|
| | |none | 0|acc_norm|0.4498|± |0.0304|
| - agieval_sat_en | 1|none | 0|acc |0.6650|± |0.0330|
| | |none | 0|acc_norm|0.5922|± |0.0343|
| - agieval_sat_en_without_passage| 1|none | 0|acc |0.4612|± |0.0348|
| | |none | 0|acc_norm|0.3932|± |0.0341|
| - agieval_sat_math | 1|none | 0|acc |0.3273|± |0.0317|
| | |none | 0|acc_norm|0.2818|± |0.0304| |