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RichardErkhov/Stopwolf_-_Cerberus-7B-slerp-gguf
Stopwolf_-_Cerberus-7B-slerp-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
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.gguf95.2 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Cerberus-7B-slerp - GGUF
| Name | Quant method | Size |
|---|---|---|
| Cerberus-7B-slerp.Q2_K.gguf | Q2_K | 2.53GB |
| Cerberus-7B-slerp.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Cerberus-7B-slerp.IQ3_S.gguf | IQ3_S | 2.96GB |
| Cerberus-7B-slerp.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Cerberus-7B-slerp.IQ3_M.gguf | IQ3_M | 3.06GB |
| Cerberus-7B-slerp.Q3_K.gguf | Q3_K | 3.28GB |
| Cerberus-7B-slerp.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Cerberus-7B-slerp.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Cerberus-7B-slerp.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Cerberus-7B-slerp.Q4_0.gguf | Q4_0 | 3.83GB |
| Cerberus-7B-slerp.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Cerberus-7B-slerp.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Cerberus-7B-slerp.Q4_K.gguf | Q4_K | 4.07GB |
| Cerberus-7B-slerp.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Cerberus-7B-slerp.Q4_1.gguf | Q4_1 | 4.24GB |
| Cerberus-7B-slerp.Q5_0.gguf | Q5_0 | 4.65GB |
| Cerberus-7B-slerp.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Cerberus-7B-slerp.Q5_K.gguf | Q5_K | 4.78GB |
| Cerberus-7B-slerp.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Cerberus-7B-slerp.Q5_1.gguf | Q5_1 | 5.07GB |
| Cerberus-7B-slerp.Q6_K.gguf | Q6_K | 5.53GB |
| Cerberus-7B-slerp.Q8_0.gguf | Q8_0 | 7.17GB |
license: apache-2.0 tags:
Cerberus-7B-slerp is a merge of the following models using mergekit:
slices:
- sources:
- model: fblgit/UNA-TheBeagle-7b-v1
layer_range: [0, 32]
- model: UCLA-AGI/zephyr-7b-sft-full-SPIN-iter3
layer_range: [0, 32]
merge_method: slerp
base_model: fblgit/UNA-TheBeagle-7b-v1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 63.46 |
| AI2 Reasoning Challenge (25-Shot) | 69.54 |
| HellaSwag (10-Shot) | 87.33 |
| MMLU (5-Shot) | 63.25 |
| TruthfulQA (0-shot) | 61.35 |
| Winogrande (5-shot) | 81.29 |
| GSM8k (5-shot) | 17.97 |