Quantization made by Richard Erkhov.
OpenCerebrum-1.0-7b-SFT - GGUF
Original model description:
language:
- en
license: apache-2.0
tags:
- open-source
- code
- math
- chemistry
- biology
- text-generation
- question-answering
datasets:
- Open-Orca/SlimOrca
- glaiveai/glaive-code-assistant
- camel-ai/physics
- camel-ai/math
- camel-ai/chemistry
- camel-ai/biology
- WizardLM/WizardLM_evol_instruct_V2_196k
- microsoft/orca-math-word-problems-200k
- grimulkan/theory-of-mind
- Vezora/Tested-22k-Python-Alpaca
- m-a-p/Code-Feedback
- Locutusque/arc-cot
- jondurbin/airoboros-2.1
- WizardLM/WizardLM_evol_instruct_70k
pipeline_tag: text-generation
OpenCerebrum-1.0-7B-SFT
OpenCerebrum-1.0-7B-SFT 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 AetherResearch's proprietary Cerebrum model.
The model was fine-tuned on approximately 1.2 million examples across 14 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.
Model Details
- Base Model: alpindale/Mistral-7B-v0.2-hf
- Parameters: 7 billion
- Fine-Tuning Dataset Size: ~1,200,000 examples
- Fine-Tuning Data: Amalgamation of 14 public datasets
- Language: English
- License: Apache 2.0
Intended Use
OpenCerebrum-1.0-7B-SFT 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 1.2 million 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 14 datasets listed in the Datasets section, totaling approximately 1.2 million examples. Default training hyperparameters were used. In the future, the fine-tuning dataset may be condensed to more closely match the 5,000 example dataset reputedly used for the original Cerebrum model.
