4bit GPTQ of:
Manticore-13b-Chat-Pyg by
openaccess-ai-collective with the Guanaco 13b qLoRa by
TimDettmers applied through
Monero, quantized by
mindrage, uncensored
Quantized to 4bit GPTQ, groupsize 128, no-act-order.
Command used to quantize:
python3 llama.py Manticore-13B-Chat-Pyg-Guanaco-GPTQ-4bit-128g.no-act-order.safetensors c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors
The model seems to have noticeably benefited from further augmentation with the Guanaco qLora.
Its capabilities seem broad, even compared with other Wizard or Manticore models, with expected weaknesses at coding. It is very good at in-context-learning and (in its class) reasoning.
It both follows instructions well, and can be used as a chatbot.
Refreshingly, it does not seem to insist on aggressively sticking to narratives to justify formerly hallucinated output as much as similar models. It's output seems... eerily smart at times.
I believe the model is fully unrestricted/uncensored and will generally not berate.
Prompting style + settings:
Presumably due to the very diverse training-data the model accepts a variety of prompting styles with relatively few issues, including the ###-Variant, but seems to work best using:
"Naming" the model works great by simply modifying the context. Substantial changes in its behaviour can be caused by appending to "ASSISTANT:", like "ASSISTANT: After careful consideration, thinking step-by-step, my response is:"
user: "USER:" -
bot: "ASSISTANT:" -
context: "This is a conversation between an advanced AI and a human user."
Turn Template: <|user|> <|user-message|>\n<|bot|><|bot-message|>\n
Settings that work well without (subjectively) being too deterministic:
temp: 0.15 -
top_p: 0.1 -
top_k: 40 -
rep penalty: 1.1
