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RichardErkhov/Azazelle_-_MN-Halide-12b-v1.0-gguf
Azazelle_-_MN-Halide-12b-v1.0-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
179
1% of all-time downloads
All-time downloads
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141 GB
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3
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.gguf163 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
MN-Halide-12b-v1.0 - GGUF
| Name | Quant method | Size |
|---|---|---|
| MN-Halide-12b-v1.0.Q2_K.gguf | Q2_K | 4.46GB |
| MN-Halide-12b-v1.0.IQ3_XS.gguf | IQ3_XS | 4.94GB |
| MN-Halide-12b-v1.0.IQ3_S.gguf | IQ3_S | 5.18GB |
| MN-Halide-12b-v1.0.Q3_K_S.gguf | Q3_K_S | 5.15GB |
| MN-Halide-12b-v1.0.IQ3_M.gguf | IQ3_M | 5.33GB |
| MN-Halide-12b-v1.0.Q3_K.gguf | Q3_K | 5.67GB |
| MN-Halide-12b-v1.0.Q3_K_M.gguf | Q3_K_M | 5.67GB |
| MN-Halide-12b-v1.0.Q3_K_L.gguf | Q3_K_L | 6.11GB |
| MN-Halide-12b-v1.0.IQ4_XS.gguf | IQ4_XS | 6.33GB |
| MN-Halide-12b-v1.0.Q4_0.gguf | Q4_0 | 6.59GB |
| MN-Halide-12b-v1.0.IQ4_NL.gguf | IQ4_NL | 6.65GB |
| MN-Halide-12b-v1.0.Q4_K_S.gguf | Q4_K_S | 6.63GB |
| MN-Halide-12b-v1.0.Q4_K.gguf | Q4_K | 6.96GB |
| MN-Halide-12b-v1.0.Q4_K_M.gguf | Q4_K_M | 6.96GB |
| MN-Halide-12b-v1.0.Q4_1.gguf | Q4_1 | 7.26GB |
| MN-Halide-12b-v1.0.Q5_0.gguf | Q5_0 | 7.93GB |
| MN-Halide-12b-v1.0.Q5_K_S.gguf | Q5_K_S | 7.93GB |
| MN-Halide-12b-v1.0.Q5_K.gguf | Q5_K | 8.13GB |
| MN-Halide-12b-v1.0.Q5_K_M.gguf | Q5_K_M | 8.13GB |
| MN-Halide-12b-v1.0.Q5_1.gguf | Q5_1 | 8.61GB |
| MN-Halide-12b-v1.0.Q6_K.gguf | Q6_K | 9.37GB |
| MN-Halide-12b-v1.0.Q8_0.gguf | Q8_0 | 12.13GB |
base_model:
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using SillyTilly/mistralai_Mistral-Nemo-Base-2407 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: SillyTilly/mistralai_Mistral-Nemo-Base-2407
dtype: float32
merge_method: model_stock
slices:
- sources:
- layer_range: [0, 40]
model: nbeerbower/Lyra4-Gutenberg-12B
- layer_range: [0, 40]
model: nbeerbower/mistral-nemo-gutenberg-12B-v4
- layer_range: [0, 40]
model: elinas/Chronos-Gold-12B-1.0
- layer_range: [0, 40]
model: UsernameJustAnother/Nemo-12B-Marlin-v8
- layer_range: [0, 40]
model: TheDrummer/Rocinante-12B-v1.1
- layer_range: [0, 40]
model: Epiculous/Azure_Dusk-v0.2
- layer_range: [0, 40]
model: Epiculous/Crimson_Dawn-v0.2
- layer_range: [0, 40]
model: TheDrummer/Rocinante-12B-v1+jtatman/mistral_nemo_12b_reasoning_psychology_lora
- layer_range: [0, 40]
model: nbeerbower/mistral-nemo-wissenschaft-12B
- layer_range: [0, 40]
model: nbeerbower/mistral-nemo-bophades-12B
- layer_range: [0, 40]
model: anthracite-org/magnum-v2.5-12b-kto+mpasila/Mistral-freeLiPPA-LoRA-12B
- layer_range: [0, 40]
model: nbeerbower/mistral-nemo-cc-12B
- layer_range: [0, 40]
model: anthracite-org/magnum-v2-12b
- layer_range: [0, 40]
model: anthracite-org/magnum-v2.5-12b-kto+jeiku/Aura-NeMo-12B
- layer_range: [0, 40]
model: SillyTilly/mistralai_Mistral-Nemo-Base-2407
tokenizer_source: unsloth/Mistral-Nemo-Base-2407
Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more quants, at much higher speed, than I would otherwise be able to.