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RichardErkhov/Inv_-_Konstanta-V4-Alpha-7B-gguf
Inv_-_Konstanta-V4-Alpha-7B-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
1.3K
9% of all-time downloads
All-time downloads
15.4K
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82.2 GB
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.gguf95.2 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Konstanta-V4-Alpha-7B - GGUF
| Name | Quant method | Size |
|---|---|---|
| Konstanta-V4-Alpha-7B.Q2_K.gguf | Q2_K | 2.53GB |
| Konstanta-V4-Alpha-7B.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Konstanta-V4-Alpha-7B.IQ3_S.gguf | IQ3_S | 2.96GB |
| Konstanta-V4-Alpha-7B.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Konstanta-V4-Alpha-7B.IQ3_M.gguf | IQ3_M | 3.06GB |
| Konstanta-V4-Alpha-7B.Q3_K.gguf | Q3_K | 3.28GB |
| Konstanta-V4-Alpha-7B.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Konstanta-V4-Alpha-7B.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Konstanta-V4-Alpha-7B.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Konstanta-V4-Alpha-7B.Q4_0.gguf | Q4_0 | 3.83GB |
| Konstanta-V4-Alpha-7B.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Konstanta-V4-Alpha-7B.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Konstanta-V4-Alpha-7B.Q4_K.gguf | Q4_K | 4.07GB |
| Konstanta-V4-Alpha-7B.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Konstanta-V4-Alpha-7B.Q4_1.gguf | Q4_1 | 4.24GB |
| Konstanta-V4-Alpha-7B.Q5_0.gguf | Q5_0 | 4.65GB |
| Konstanta-V4-Alpha-7B.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Konstanta-V4-Alpha-7B.Q5_K.gguf | Q5_K | 4.78GB |
| Konstanta-V4-Alpha-7B.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Konstanta-V4-Alpha-7B.Q5_1.gguf | Q5_1 | 5.07GB |
| Konstanta-V4-Alpha-7B.Q6_K.gguf | Q6_K | 5.53GB |
| Konstanta-V4-Alpha-7B.Q8_0.gguf | Q8_0 | 7.17GB |
base_model:
This is a merge of pre-trained language models created using mergekit.
Alright, so, this model seems to be REALLY good. Konstanta-7B is pretty good either, but this one is still marginally better.
This model was merged using the DARE TIES merge method using Inv/Konstanta-7B as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
merge_method: dare_ties
dtype: bfloat16
parameters:
int8_mask: true
base_model: Inv/Konstanta-7B
models:
- model: Inv/Konstanta-7B
- model: KatyTheCutie/LemonadeRP-4.5.3
parameters:
density: 0.65
weight: [0.65, 0.40, 0.35, 0.30, 0.35, 0.40, 0.25]
- model: senseable/WestLake-7B-v2
parameters:
density: 0.85
weight: [0.25, 0.40, 0.35, 0.30, 0.35, 0.40, 0.65]