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--- |
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language: |
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- en |
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thumbnail: null |
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tags: |
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- text generation |
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- instruct |
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pipeline_tag: text-generation |
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inference: false |
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license: llama2 |
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datasets: |
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- PygmalionAI/PIPPA |
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- Open-Orca/OpenOrca |
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- Norquinal/claude_multiround_chat_30k |
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- jondurbin/airoboros-gpt4-1.4.1 |
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- databricks/databricks-dolly-15k |
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--- |
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<h1 style="text-align: center">Pygmalion-2 7B</h1> |
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<h2 style="text-align: center">An instruction-tuned Llama-2 biased towards fiction writing and conversation.</h2> |
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## Model Details |
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The long-awaited release of our new models based on Llama-2 is finally here. Pygmalion-2 7B (formerly known as Metharme) is based on |
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[Llama-2 7B](https://huggingface.co/meta-llama/llama-2-7b-hf) released by Meta AI. |
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The Metharme models were an experiment to try and get a model that is usable for conversation, roleplaying and storywriting, |
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but which can be guided using natural language like other instruct models. After much deliberation, we reached the conclusion |
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that the Metharme prompting format is superior (and easier to use) compared to the classic Pygmalion. |
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This model was trained by doing supervised fine-tuning over a mixture of regular instruction data alongside roleplay, fictional stories |
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and conversations with synthetically generated instructions attached. |
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This model is freely available for both commercial and non-commercial use, as per the Llama-2 license. |
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## Prompting |
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The model has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. |
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The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. |
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The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to |
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form a conversation history. |
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### Prompting example |
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The system prompt has been designed to allow the model to "enter" various modes and dictate the reply length. Here's an example: |
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``` |
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<|system|>Enter RP mode. Pretend to be {{char}} whose persona follows: |
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{{persona}} |
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You shall reply to the user while staying in character, and generate long responses. |
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``` |
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## Dataset |
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The dataset used to fine-tune this model includes our own [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA), along with several other instruction |
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datasets, and datasets acquired from various RP forums. |
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## Limitations and biases |
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The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. |
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As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that |
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are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |
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Outputs might often be factually wrong or misleading. |
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## Acknowledgements |
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We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for this model. |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_PygmalionAI__pygmalion-2-7b) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 44.66 | |
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| ARC (25-shot) | 54.01 | |
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| HellaSwag (10-shot) | 78.23 | |
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| MMLU (5-shot) | 49.11 | |
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| TruthfulQA (0-shot) | 43.78 | |
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| Winogrande (5-shot) | 75.14 | |
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| GSM8K (5-shot) | 6.37 | |
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| DROP (3-shot) | 5.98 | |
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