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license: agpl-3.0
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---
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This is converted and quantized from [Pygmalion 1.3B](https://huggingface.co/PygmalionAI/pygmalion-1.3b), based on [an earlier version
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# RAM USAGE (on KoboldCpp w/ OpenBLAS)
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Model | Initial RAM
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ggml-pygmalion-1.3b-q4_0.bin | 1.1 GiB
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ggml-pygmalion-1.3b-q5_1.bin | 1.3 GiB
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---
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license: agpl-3.0
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language:
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- en
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thumbnail:
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tags:
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- text generation
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- conversational
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inference: false
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*(Not to be confused with [Pygmalion 13B](https://huggingface.co/TehVenom/Pygmalion-13b-GGML).)*
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This is converted and quantized from [Pygmalion 1.3B](https://huggingface.co/PygmalionAI/pygmalion-1.3b), based on [an earlier version](https://huggingface.co/EleutherAI/pythia-1.4b-deduped-v0) of Pythia 1.4B Deduped.
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# RAM USAGE (on KoboldCpp w/ OpenBLAS)
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Model | Initial RAM
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:--:|:--:
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ggml-pygmalion-1.3b-q4_0.bin | 1.1 GiB
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ggml-pygmalion-1.3b-q5_1.bin | 1.3 GiB
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Below is the original model card.
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* * *
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# Pygmalion 1.3B
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## Model description
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Pymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's [pythia-1.3b-deduped](https://huggingface.co/EleutherAI/pythia-1.3b-deduped).
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**Warning:** This model is **NOT** suitable for use by minors. It **will** output X-rated content under certain circumstances.
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## Training data
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The fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations.
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## Training procedure
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Fine-tuning was done using [ColossalAI](https://github.com/hpcaitech/ColossalAI) (specifically, with a slightly modified version of their [OPT fine-tune example](https://github.com/hpcaitech/ColossalAI/blob/78509124d32b63b7fc36f6508e0576a326d51422/examples/language/opt/run_clm.py)) for around 11.4 million tokens over 5440 steps on a single 24GB GPU. The run took just under 21 hours.
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## Intended use
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### The easy way
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We provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found [here](https://github.com/PygmalionAI/gradio-ui/blob/master/notebooks/GPU.ipynb).
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### The manual way
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The model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format:
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```
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[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
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[DIALOGUE HISTORY]
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You: [Your input message here]
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[CHARACTER]:
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```
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Where `[CHARACTER] `is, as you can probably guess, the name of the character you want the model to portray, and `[DIALOGUE HISTORY]` is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like:
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```
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[CHARACTER]: [some dialogue here]
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You: [your response to the dialogue above]
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```
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Apart from chat history, you can also just add example conversations in `[DIALOGUE HISTORY]` to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition.
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## Known issues
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- The model can get stuck repeating certain phrases, or sometimes even entire sentences.
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- We believe this is due to that behavior being present in the training data itself, and plan to investigate and adjust accordingly for future versions.
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