Create README.md

README.md

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+---
+datasets:
+- anon8231489123/ShareGPT_Vicuna_unfiltered
+- ehartford/wizard_vicuna_70k_unfiltered
+- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
+- QingyiSi/Alpaca-CoT
+- teknium/GPT4-LLM-Cleaned
+- teknium/GPTeacher-General-Instruct
+- metaeval/ScienceQA_text_only
+- hellaswag
+- openai/summarize_from_feedback
+- riddle_sense
+- gsm8k
+- ewof/code-alpaca-instruct-unfiltered
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Manticore 30B Chat (ALPHA)
+
+- Alpha release of checkpoint before train and eval loss spikes. Additionally, there seems to be some alignment which is easily jailbroken.
+
+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 
+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.
+
+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)
+
+# Training Datasets
+
+Manticore 30B Chat is a Llama 30B model fine-tuned on the following datasets along with the datasets from the original Manticore 30B. 
+
+**Manticore 30B Chat was trained on effectively 40% of the datasets below due to only training for 0.4 epochs.
+
+- de-duped pygmalion dataset, filtered down to RP data
+- [riddle_sense](https://huggingface.co/datasets/riddle_sense) - instruct augmented 
+- hellaswag, updated for detailed explanations w 30K+ rows
+- [gsm8k](https://huggingface.co/datasets/gsm8k) - instruct augmented 
+- [ewof/code-alpaca-instruct-unfiltered](https://huggingface.co/datasets/ewof/code-alpaca-instruct-unfiltered)
+
+Manticore 30B
+- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset
+- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
+- [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
+- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
+- [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned)
+- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
+- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses, derived from the `train` split
+- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses, derived from the `train` split
+- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
+- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization
+
+Not added from Manticore 13B:
+- mmlu - mmlu datasets were not added to this model as the `test` split is used for benchmarks
+
+# Shoutouts
+
+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.
+# Demo
+
+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.
+- https://huggingface.co/spaces/openaccess-ai-collective/manticore-13b-chat-pyg
+
+## Release Notes
+
+- https://wandb.ai/wing-lian/manticore-13b-v2/runs/ij10c6m3
+
+## Build
+
+Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB 
+ - 0.4 epochs taking approximately 14 hours. No further epochs will be released for the alpha. 
+ - 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).
+
+## Bias, Risks, and Limitations
+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).
+Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.
+
+## Examples
+
+TBD