(Not to be confused with Pygmalion 13B and Pygmalion 2 13B.)
Pygmalion 1.3B GGML
This repository contains quantized conversions of the Pygmalion 1.3B checkpoint.
For use with frontends that support GGML quantized GPT-NeoX models, such as KoboldCpp and Oobabooga (with the CTransformers loader).
Last updated on 2023-09-23.
| Model | Startup RAM usage (KoboldCpp) | Startup RAM usage (Oobabooga) |
|---|---|---|
| pygmalion-1.3b.q4_0.bin | 1.0 GiB | 1.3 GiB |
| pygmalion-1.3b.q4_1.bin | 1.1 GiB | 1.4 GiB |
| pygmalion-1.3b.q5_0.bin | 1.2 GiB | 1.5 GiB |
| pygmalion-1.3b.q5_1.bin | 1.3 GiB | 1.6 GiB |
| pygmalion-1.3b.q8_0.bin | 1.7 GiB | 2.0 GiB |
| pygmalion-1.3b.f16.bin | 2.9 GiB | 3.2 GiB |
Recommended settings:
Pygmalion 1.3B is a limited model, left in the dust by the Pygmalion project's advancements since then. Which is a shame, as it remains one of the few conversational models available for systems with less than 2GB RAM, at least before we get TinyLLaMA and quantized Phi-1.5.
Here are some tips to get the best results you can out of this model:
- Stick to a low temperature, preferably between 0.2 and 0.7.
- Keep your repetition penalty between 1.0 and 1.02. These tiny values are required for models based on Pythia Deduped.
- If using SillyTavern, follow these settings:
- You also have to keep character descriptions to a few sentences, possibly following CharacterAI's 500-character descriptions.
Notes:
- KoboldCpp [bfc696f] was tested without OpenBLAS.
- Oobabooga [895ec9d] was tested with with the
--model <model> --loader ctransformers --model_type gptneoxlaunch arguments. - ggerganov/ggml [8ca2c19] was used for conversion and quantization.
- The original model is available at PygmalionAI/pygmalion-1.3b.
- Earlier ggmlv2 quantizations are available here.
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.