Quantization made by Richard Erkhov.
pygmalion-1.3b - GGUF
- Model creator: https://huggingface.co/PygmalionAI/
- Original model: https://huggingface.co/PygmalionAI/pygmalion-1.3b/
| Name | Quant method | Size |
|---|---|---|
| pygmalion-1.3b.Q2_K.gguf | Q2_K | 0.53GB |
| pygmalion-1.3b.IQ3_XS.gguf | IQ3_XS | 0.59GB |
| pygmalion-1.3b.IQ3_S.gguf | IQ3_S | 0.61GB |
| pygmalion-1.3b.Q3_K_S.gguf | Q3_K_S | 0.61GB |
| pygmalion-1.3b.IQ3_M.gguf | IQ3_M | 0.66GB |
| pygmalion-1.3b.Q3_K.gguf | Q3_K | 0.71GB |
| pygmalion-1.3b.Q3_K_M.gguf | Q3_K_M | 0.71GB |
| pygmalion-1.3b.Q3_K_L.gguf | Q3_K_L | 0.77GB |
| pygmalion-1.3b.IQ4_XS.gguf | IQ4_XS | 0.74GB |
| pygmalion-1.3b.Q4_0.gguf | Q4_0 | 0.77GB |
| pygmalion-1.3b.IQ4_NL.gguf | IQ4_NL | 0.78GB |
| pygmalion-1.3b.Q4_K_S.gguf | Q4_K_S | 0.78GB |
| pygmalion-1.3b.Q4_K.gguf | Q4_K | 0.85GB |
| pygmalion-1.3b.Q4_K_M.gguf | Q4_K_M | 0.85GB |
| pygmalion-1.3b.Q4_1.gguf | Q4_1 | 0.85GB |
| pygmalion-1.3b.Q5_0.gguf | Q5_0 | 0.92GB |
| pygmalion-1.3b.Q5_K_S.gguf | Q5_K_S | 0.92GB |
| pygmalion-1.3b.Q5_K.gguf | Q5_K | 0.98GB |
| pygmalion-1.3b.Q5_K_M.gguf | Q5_K_M | 0.98GB |
| pygmalion-1.3b.Q5_1.gguf | Q5_1 | 1.0GB |
| pygmalion-1.3b.Q6_K.gguf | Q6_K | 1.08GB |
| pygmalion-1.3b.Q8_0.gguf | Q8_0 | 1.4GB |
Original model description:
license: agpl-3.0 language:
- en thumbnail: tags:
- text generation
- conversational inference: false
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.