83 lines
4.5 KiB
Markdown
83 lines
4.5 KiB
Markdown
---
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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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- openai/summarize_from_feedback
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- riddle_sense
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- gsm8k
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- ewof/code-alpaca-instruct-unfiltered
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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 30B Chat (ALPHA)
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- Alpha release of checkpoint before train and eval loss spikes. Additionally, there seems to be some alignment which is easily jailbroken.
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**[💵 Donate to OpenAccess AI Collective](https://github.com/sponsors/OpenAccess-AI-Collective) to help us keep building great tools and models!**
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Manticore 30B Chat builds on Manticore v1 with new datasets, including a de-duped subset of the Pygmalion dataset. It also removes all Alpaca style prompts using `###` in favor of
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chat only style prompts using `USER:`,`ASSISTANT:` as well as [pygmalion/metharme prompting](https://huggingface.co/PygmalionAI/metharme-7b#prompting) using `<|system|>, <|user|> and <|model|>` tokens.
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Questions, comments, feedback, looking to donate, or want to help? Reach out on our [Discord](https://discord.gg/EqrvvehG) or email [wing@openaccessaicollective.org](mailto:wing@openaccessaicollective.org)
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# Training Datasets
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Manticore 30B Chat is a Llama 30B model fine-tuned on the following datasets along with the datasets from the original Manticore 30B.
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**Manticore 30B Chat was trained on effectively 40% of the datasets below due to only training for 0.4 epochs.
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- de-duped pygmalion dataset, filtered down to RP data
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- [riddle_sense](https://huggingface.co/datasets/riddle_sense) - instruct augmented
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- hellaswag, updated for detailed explanations w 30K+ rows
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- [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented
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- [ewof/code-alpaca-instruct-unfiltered](https://huggingface.co/datasets/ewof/code-alpaca-instruct-unfiltered)
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Manticore 30B
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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, derived from the `train` split
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- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses, derived from the `train` split
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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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Not added from Manticore 13B:
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- mmlu - mmlu datasets were not added to this model as the `test` split is used for benchmarks
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# Shoutouts
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Special thanks to Nanobit for helping with Axolotl, TheBloke for quantizing these models are more accessible to all, ehartford for cleaned datasets, and 0x000011b for the RP dataset.
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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-13b-chat-pyg
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## Release Notes
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- https://wandb.ai/wing-lian/manticore-13b-v2/runs/ij10c6m3
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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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- 0.4 epochs taking approximately 14 hours. No further epochs will be released for the alpha.
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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-30b-chat-pyg-alpha/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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TBD |