Nexes the Elder
Nexesenex
AI & ML interests
Maintaining KoboldCPP fork Croco.Cpp, and Lllama.cpp fork NXS_Llama.cpp
Merging models sometimes.
Making quants of my merges and fav models, often specific to IKL/NXSL/Croco.Cpp.
Barking at many trees.
Thanks to Mradermacher and Bartowski for their imatrixes, I'm using them extensively.
Note : Don't make quants of my merges until they are versioned! (Vx or Vx.x)
Recent Activity
updated
a model
about 6 hours ago
NexesQuants/DeepSeek-V2-Lite-Chat-Q8_0-GGUF
published
a model
about 6 hours ago
NexesQuants/DeepSeek-V2-Lite-Chat-Q8_0-GGUF
updated
a model
about 6 hours ago
NexesQuants/DeepSeek-V2-Lite-Chat-Uncensored-Q8_0-GGUF
Organizations
Legacy L3 70b models merged with Llama 3.1 70b Tess 3 (credit: Miguel Tissera) for 128K context capability and low perplexity.
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Nexesenex/Llama_3.x_70b_L3.3_Dolphin_128K_v1.02
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_Tess_Dolphin_128K_v1.2
Text Generation • 71B • Updated • 4 • 1 -
Nexesenex/Llama_3.x_70b_Tess_Cat_128K_v1.0
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_L3.3_Athene_128K_v1.02
Text Generation • 71B • Updated • 4 • 1
How I select models ? First, I bench them, to trim the overfit & dumbified models. Then, I test them. The smartest to my taste end-up here.
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NexesQuants/alchemonaut_QuartetAnemoi-70B-iMat.GGUF
69B • Updated • 63 • 11 -
NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF
69B • Updated • 860 • 70 -
NexesQuants/TeeZee_Kyllene-Yi-34B-v1.1-iMat.GGUF
34B • Updated • 620 • 25 -
Nexesenex/TomGrc_FusionNet_7Bx2_MoE_v0.1-iMat.GGUF
13B • Updated • 49 • 2
My merges worthy of being downloaded and used.
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Nexesenex/Llama_3.x_70b_Hexagon_Blue_V1
Text Generation • 71B • Updated • 6 • 1 -
Nexesenex/Llama_3.x_70b_SmarTricks_v1.30_flat
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_Hexagon_Purple_V2
Text Generation • 71B • Updated • 752 • 2
With particular features..
My merges worthy of being downloaded and used.
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Nexesenex/Llama_3.x_70b_Hexagon_Blue_V1
Text Generation • 71B • Updated • 6 • 1 -
Nexesenex/Llama_3.x_70b_SmarTricks_v1.30_flat
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_Hexagon_Purple_V2
Text Generation • 71B • Updated • 752 • 2
Legacy L3 70b models merged with Llama 3.1 70b Tess 3 (credit: Miguel Tissera) for 128K context capability and low perplexity.
-
Nexesenex/Llama_3.x_70b_L3.3_Dolphin_128K_v1.02
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_Tess_Dolphin_128K_v1.2
Text Generation • 71B • Updated • 4 • 1 -
Nexesenex/Llama_3.x_70b_Tess_Cat_128K_v1.0
Text Generation • 71B • Updated • 4 -
Nexesenex/Llama_3.x_70b_L3.3_Athene_128K_v1.02
Text Generation • 71B • Updated • 4 • 1
With particular features..
How I select models ? First, I bench them, to trim the overfit & dumbified models. Then, I test them. The smartest to my taste end-up here.
-
NexesQuants/alchemonaut_QuartetAnemoi-70B-iMat.GGUF
69B • Updated • 63 • 11 -
NexesQuants/MIstral-QUantized-70b_Miqu-1-70b-iMat.GGUF
69B • Updated • 860 • 70 -
NexesQuants/TeeZee_Kyllene-Yi-34B-v1.1-iMat.GGUF
34B • Updated • 620 • 25 -
Nexesenex/TomGrc_FusionNet_7Bx2_MoE_v0.1-iMat.GGUF
13B • Updated • 49 • 2