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RichardErkhov/maywell_-_PiVoT-MoE-gguf
maywell_-_PiVoT-MoE-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
7
1% of all-time downloads
All-time downloads
633
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360 GB
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.gguf425 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
PiVoT-MoE - GGUF
| Name | Quant method | Size |
|---|---|---|
| PiVoT-MoE.Q2_K.gguf | Q2_K | 12.28GB |
| PiVoT-MoE.Q3_K_S.gguf | Q3_K_S | 14.5GB |
| PiVoT-MoE.Q3_K.gguf | Q3_K | 16.1GB |
| PiVoT-MoE.Q3_K_M.gguf | Q3_K_M | 16.1GB |
| PiVoT-MoE.Q3_K_L.gguf | Q3_K_L | 17.45GB |
| PiVoT-MoE.IQ4_XS.gguf | IQ4_XS | 18.13GB |
| PiVoT-MoE.Q4_0.gguf | Q4_0 | 18.95GB |
| PiVoT-MoE.IQ4_NL.gguf | IQ4_NL | 19.13GB |
| PiVoT-MoE.Q4_K_S.gguf | Q4_K_S | 19.11GB |
| PiVoT-MoE.Q4_K.gguf | Q4_K | 20.33GB |
| PiVoT-MoE.Q4_K_M.gguf | Q4_K_M | 20.33GB |
| PiVoT-MoE.Q4_1.gguf | Q4_1 | 21.04GB |
| PiVoT-MoE.Q5_0.gguf | Q5_0 | 23.13GB |
| PiVoT-MoE.Q5_K_S.gguf | Q5_K_S | 23.13GB |
| PiVoT-MoE.Q5_K.gguf | Q5_K | 23.84GB |
| PiVoT-MoE.Q5_K_M.gguf | Q5_K_M | 23.84GB |
| PiVoT-MoE.Q5_1.gguf | Q5_1 | 25.23GB |
| PiVoT-MoE.Q6_K.gguf | Q6_K | 27.58GB |
| PiVoT-MoE.Q8_0.gguf | Q8_0 | 35.73GB |

PiVoT-MoE, is an advanced AI model specifically designed for roleplaying purposes. It has been trained using a combination of four 10.7B sized experts, each with their own specialized characteristic, all fine-tuned to bring a unique and diverse roleplaying experience.
The Mixture of Experts (MoE) technique is utilized in this model, allowing the experts to work together synergistically, resulting in a more cohesive and natural conversation flow. The MoE architecture allows for a higher level of flexibility and adaptability, enabling PiVoT-MoE to handle a wide variety of roleplaying scenarios and characters.
Based on the PiVoT-10.7B-Mistral-v0.2-RP model, PiVoT-MoE takes it a step further with the incorporation of the MoE technique. This means that not only does the model have an expansive knowledge base, but it also has the ability to mix and match its expertise to better suit the specific roleplaying scenario.
{system}
### Instruction:
{instruction}
### Response:
{response}