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YAML Metadata Warning: The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other

Pygmalion 1.3B

Model description

Pymalion 1.3B is a proof-of-concept dialogue model based on EleutherAI's pythia-1.3b-deduped.

Warning: This model is NOT suitable for use by minors. It will output X-rated content under certain circumstances.

Training data

The fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real and partially machine-generated conversations.

Training procedure

Fine-tuning was done using ColossalAI (specifically, with a slightly modified version of their OPT fine-tune example) for around 11.4 million tokens over 5440 steps on a single 24GB GPU. The run took just under 21 hours.

Intended use

The easy way

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.

The manual way

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:

[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]

[DIALOGUE HISTORY]
You: [Your input message here]
[CHARACTER]:

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:

[CHARACTER]: [some dialogue here]
You: [your response to the dialogue above]

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.

Known issues

  • The model can get stuck repeating certain phrases, or sometimes even entire sentences.
    • 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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