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RichardErkhov/netcat420_-_MFANN3bv0.20.14-gguf
netcat420_-_MFANN3bv0.20.14-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
56
8% of all-time downloads
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.gguf37.4 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
MFANN3bv0.20.14 - GGUF
| Name | Quant method | Size |
|---|---|---|
| MFANN3bv0.20.14.Q2_K.gguf | Q2_K | 1.03GB |
| MFANN3bv0.20.14.IQ3_XS.gguf | IQ3_XS | 1.12GB |
| MFANN3bv0.20.14.IQ3_S.gguf | IQ3_S | 1.16GB |
| MFANN3bv0.20.14.Q3_K_S.gguf | Q3_K_S | 1.16GB |
| MFANN3bv0.20.14.IQ3_M.gguf | IQ3_M | 1.23GB |
| MFANN3bv0.20.14.Q3_K.gguf | Q3_K | 1.33GB |
| MFANN3bv0.20.14.Q3_K_M.gguf | Q3_K_M | 1.33GB |
| MFANN3bv0.20.14.Q3_K_L.gguf | Q3_K_L | 1.47GB |
| MFANN3bv0.20.14.IQ4_XS.gguf | IQ4_XS | 1.43GB |
| MFANN3bv0.20.14.Q4_0.gguf | Q4_0 | 1.49GB |
| MFANN3bv0.20.14.IQ4_NL.gguf | IQ4_NL | 1.5GB |
| MFANN3bv0.20.14.Q4_K_S.gguf | Q4_K_S | 1.51GB |
| MFANN3bv0.20.14.Q4_K.gguf | Q4_K | 1.62GB |
| MFANN3bv0.20.14.Q4_K_M.gguf | Q4_K_M | 1.62GB |
| MFANN3bv0.20.14.Q4_1.gguf | Q4_1 | 1.65GB |
| MFANN3bv0.20.14.Q5_0.gguf | Q5_0 | 1.8GB |
| MFANN3bv0.20.14.Q5_K_S.gguf | Q5_K_S | 1.8GB |
| MFANN3bv0.20.14.Q5_K.gguf | Q5_K | 1.87GB |
| MFANN3bv0.20.14.Q5_K_M.gguf | Q5_K_M | 1.87GB |
| MFANN3bv0.20.14.Q5_1.gguf | Q5_1 | 1.95GB |
| MFANN3bv0.20.14.Q6_K.gguf | Q6_K | 2.13GB |
| MFANN3bv0.20.14.Q8_0.gguf | Q8_0 | 2.75GB |
base_model:
This is a merge of pre-trained language models created using mergekit.
This model was merged using the TIES merge method using liminerity/Phigments12 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: netcat420/MFANN3bv0.20.13
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: netcat420/MFANN3bv0.20
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
merge_method: ties
base_model: liminerity/Phigments12
parameters:
normalize: true
int8_mask: true
dtype: bfloat16