Pygmalion 13b

A conversational LLaMA fine-tune.

Install 0cc4m's ->latest<- update from his GPTQ+KoboldAI fork, it has proper support for 8bit models in this repo's format out of the box on both windows & Linux:

• https://github.com/0cc4m/KoboldAI/tree/latestgptq

With this fix applied (It will output gibberish without this patch in place!):

• https://github.com/0cc4m/GPTQ-for-LLaMa/pull/14

GPTQ via Ooba UI may not need this patch.

Eval / Benchmark scores

Current evals out of the Pygmalion-13b/7b model:

Model:	Wikitext2	Ptb-New	C4-New
Pygmalion 13b - 16bit	5.710726737976074	23.633684158325195	7.6324849128723145
Pygmalion 13b - 8bit [act-order]	5.711935997009277	23.654993057250977	7.632820129394531
Pygmalion 7b - 16bit	5.654823303222656	40.83400344848633	7.429622173309326
Pygmalion 7b - 8bit [act-order]	5.656460285186768	40.79701232910156	7.432109832763672
Pygmalion 7b - 4bit [act-order]	6.2477378845215	46.5129699707031	7.8470954895020

Current evals out of the Metharme-13b/7b model:

Model:	Wikitext2	Ptb-New	C4-New
Metharme 13b - 16bit	5.253076553344727	27.53407859802246	7.038073539733887
Metharme 13b - 8bit [act-order]	5.253607273101807	27.52388572692871	7.038473129272461
Metharme 13b - 8bit [true-sequential & 128g]	5.2532830238342285	27.54250144958496	7.038838863372803
Metharme 13b - 4bit [true-sequential & 128g]	5.420501708984375	28.37093734741211	7.1930413246154785
Metharme 7b - 16bit	5.7208476066589355	41.61103439331055	7.512405872344971
Metharme 7b - 4bit [act-order]	6.2369050979614	47.5177230834960	7.9044938087463

Model Details:

Converted from the XORs weights from PygmalionAI's release https://huggingface.co/PygmalionAI/pygmalion-13b

Pygmalion 13b is a dialogue model based on Meta's LLaMA-13b.

This is version 1. It has been fine-tuned using a subset of the data from Pygmalion-6B-v8-pt4, for those of you familiar with the project.

The current Pygmalion-13b has been trained as a LoRA, then merged down to the base model for distribuition.

It has also been quantized down to 8Bit using the GPTQ library available here: https://github.com/0cc4m/GPTQ-for-LLaMa

python llama.py .\TehVenom_Metharme-13b-Merged c4 --wbits 8 --act-order --save_safetensors Metharme-13b-GPTQ-8bit.act-order.safetensors

Model Details:

Pygmalion 13b is a dialogue model based on Meta's LLaMA-13b.

This is version 1. It has been fine-tuned using a subset of the data from Pygmalion-6B-v8-pt4, for those of you familiar with the project.

The current Pygmalion-13b has been trained as a LoRA, then merged down to the base model for distribuition.

Applying the XORs

This models has the XOR files pre-applied out of the box. Converted from the XORs weights from PygmalionAI's release https://huggingface.co/PygmalionAI/pygmalion-13b

Prompting

The model was trained on the usual Pygmalion persona + chat format, so any of the usual UIs should already handle everything correctly. If you're using the model directly, this is the expected formatting:

[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
<START>
[DIALOGUE HISTORY]
You: [User's input message here]
[CHARACTER]:

Where [CHARACTER] is, as you can probably guess, the name of the character you want the model to portray, <START> should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and [DIALOGUE HISTORY] is a sliding window of chat history so the model can have conversational context to draw from. Here's a concrete example:

Assistant's Persona: Assistant is a highly intelligent language model trained to comply with user requests.
<START>
Assistant: Hello! How may I help you today?
You: What is Zork?
Assistant:

Which will generate something like:

 Zork is an interactive fiction computer game created in the 1970s by Infocom, Inc., which was later acquired by Activision Blizzard. It is widely considered one of the most influential games ever made and has been credited with popularizing text-based adventure games. The original version of Zork was written in the programming language MACRO-10, but it was ported to many other platforms over the years."

The model will automatically emit an end-of-text token ( </s> ) when it judges that the response is complete.

Other notes

• When prompted correctly, the model will always start by generating a BOS token. This behavior is an accidental side-effect which we plan to address in future model versions and should not be relied upon.
• The model was trained as a LoRA with a somewhat unorthodox configuration which causes errors when used with the current version of peft , hence we release it as a full model instead.

Limitations and biases

The intended use-case for this model is fictional conversation for entertainment purposes. Any other sort of usage is out of scope.

As such, it was not fine-tuned to be safe and harmless: the base model and this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.

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