Locutusque_-_OpenCerebrum-1.0-7b-DPO-gguf

Quantization made by Richard Erkhov.

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OpenCerebrum-1.0-7b-DPO - GGUF

• Model creator: https://huggingface.co/Locutusque/
• Original model: https://huggingface.co/Locutusque/OpenCerebrum-1.0-7b-DPO/

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:
    • 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

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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

Detailed results can be found here