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--- |
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datasets: |
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- anon8231489123/ShareGPT_Vicuna_unfiltered |
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- ehartford/wizard_vicuna_70k_unfiltered |
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- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered |
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- QingyiSi/Alpaca-CoT |
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- teknium/GPT4-LLM-Cleaned |
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- teknium/GPTeacher-General-Instruct |
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- metaeval/ScienceQA_text_only |
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- hellaswag |
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- tasksource/mmlu |
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- openai/summarize_from_feedback |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# Manticore 13B - Preview Release (previously Wizard Mega) |
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Manticore 13B is a Llama 13B model fine-tuned on the following datasets: |
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- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset |
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- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered) |
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- [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered) |
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- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT) |
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- [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned) |
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- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct) |
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- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses |
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- mmlu: instruct augmented for detailed responses subset including |
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- abstract_algebra |
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- conceptual_physics |
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- formal_logic |
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- high_school_physics |
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- logical_fallacies |
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- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses |
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- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses |
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- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization |
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# Demo |
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Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality. |
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- https://huggingface.co/spaces/openaccess-ai-collective/manticore-ggml |
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## Release Notes |
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- https://wandb.ai/wing-lian/manticore-13b/runs/nq3u3uoh/workspace |
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## Build |
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Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB |
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- Preview Release: 1 epoch taking 8 hours. |
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- The configuration to duplicate this build is provided in this repo's [/config folder](https://huggingface.co/openaccess-ai-collective/manticore-13b/tree/main/configs). |
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## Bias, Risks, and Limitations |
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Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). |
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Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information. |
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## Examples |
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```` |
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### Instruction: write Python code that returns the first n numbers of the Fibonacci sequence using memoization. |
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### Assistant: |
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```` |
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``` |
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### Instruction: Finish the joke, a mechanic and a car salesman walk into a bar... |
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### Assistant: |
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``` |
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