license: agpl-3.0
language:
- en
thumbnail: null
tags:
- ggml
- text generation
- conversational
inference: false
(Not to be confused with Pygmalion 13B.)
This is converted and quantized from Pygmalion 1.3B, based on an earlier version of Pythia 1.4B Deduped.
Notes:
- Converted with ggerganov/ggml's gpt-neox conversion script, and tested with KoboldCpp.
- I can't promise that this will work with other frontends, if at all. I've had problems with the tokenizer. Could be related to the ggml implementation of GPT-NeoX (source).
RAM USAGE (on KoboldCpp w/ OpenBLAS)
| Model | Initial RAM |
|---|---|
| ggml-pygmalion-1.3b-q4_0.bin | 1.1 GiB |
| ggml-pygmalion-1.3b-q5_1.bin | 1.3 GiB |
Below is the original model card for Pygmalion 1.3B.
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.