exl2 version of Norquinal/PetrolLM-CollectiveCognition
used dataset : wikitext
quantized by IHaBiS
command : python convert.py -i models/Norquinal_PetrolLM-CollectiveCognition -o Norquinal_PetrolLM-CollectiveCognition-temp -cf Norquinal_PetrolLM-CollectiveCognition-6bpw-h8-exl2 -c 0000.parquet -l 4096 -b 6 -hb 8 -ss 4096 -m Norquinal_PetrolLM-CollectiveCognition_measurement.json
Below this sentence is original model card
What is PetrolLM-Claude-Chat?
PetrolLM-Claude-Chat is the CollectiveCognition-v1.1-Mistral-7B model with the PetrolLoRA applied.
The dataset (for the LoRA) consists of 2800 samples, with the composition as follows:
- AICG Logs (~34%)
- PygmalionAI/PIPPA (~33%)
- Squish42/bluemoon-fandom-1-1-rp-cleaned (~29%)
- OpenLeecher/Teatime (~4%)
These samples were then back-filled using gpt-4/gpt-3.5-turbo-16k or otherwise converted to fit the prompt format.
Prompt Format
The model uses the following prompt format: ```
style: roleplay characters: [char]: [description] summary: [scenario]
Format: [char]: [message] Human: [message] ```Use in Text Generation Web UI
Install the bleeding-edge version of transformers from source:
pip install git+https://github.com/huggingface/transformers
Or, alternatively, change model_type in config.json from mistral to llama.
Use in SillyTavern UI
As an addendum, you can include one of the following as the Last Output Sequence:
Human: In your next reply, write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.
{{char}}:
{{char}} (2 paragraphs, engaging, natural, authentic, descriptive, creative):
[System note: Write at least two paragraphs. Be descriptive and immersive, providing vivid details about {{char}}'s actions, emotions, and the environment.]
{{char}}:
The third one seems to work the best. I would recommend experimenting with creating your own to best suit your needs.
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