---
# 4bit GPTQ of:
Manticore-13b-Chat-Pyg by [openaccess-ai-collective](https://huggingface.co/openaccess-ai-collective/manticore-13b-chat-pyg) with the Guanaco 13b qLoRa by [TimDettmers](https://huggingface.co/timdettmers/guanaco-13b) applied through [Monero](https://huggingface.co/Monero/Manticore-13b-Chat-Pyg-Guanaco), quantized by [mindrage](https://huggingface.co/mindrage), uncensored


[link to GGML Version](https://huggingface.co/mindrage/Manticore-13B-Chat-Pyg-Guanaco-GGML-q4_0)

---


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
---
{"library_name": "transformers", "tags": ["manticore", "guanaco", "uncensored"], "pipeline_tag": "text-generation"}
transformers llama text-generation manticore guanaco uncensored autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #llama #text-generation #manticore #guanaco #uncensored #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

---
# 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


link to GGML Version

---


Quantized to 4bit GPTQ, groupsize 128, no-act-order.

Command used to quantize: 
python3 URL URL-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
---
# 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


link to GGML Version

---


Quantized to 4bit GPTQ, groupsize 128, no-act-order.

Command used to quantize: 
python3 URL URL-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
---
TAGS
#transformers #llama #text-generation #manticore #guanaco #uncensored #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 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


link to GGML Version

---


Quantized to 4bit GPTQ, groupsize 128, no-act-order.

Command used to quantize: 
python3 URL URL-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
---