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nonetrix_-_sillyrp-7b-gguf
RichardErkhov/nonetrix_-_sillyrp-7b-gguf
nonetrix_-_sillyrp-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.
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.gguf95.2 GB · 100%
At a glance
- Access
- Public
- Created
- Sep 9, 2024
- Updated
- Sep 9, 2024
- SHA
- 08a6bfb7
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
sillyrp-7b - GGUF
- Model creator: https://huggingface.co/nonetrix/
- Original model: https://huggingface.co/nonetrix/sillyrp-7b/
| Name | Quant method | Size |
|---|---|---|
| sillyrp-7b.Q2_K.gguf | Q2_K | 2.53GB |
| sillyrp-7b.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| sillyrp-7b.IQ3_S.gguf | IQ3_S | 2.96GB |
| sillyrp-7b.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| sillyrp-7b.IQ3_M.gguf | IQ3_M | 3.06GB |
| sillyrp-7b.Q3_K.gguf | Q3_K | 3.28GB |
| sillyrp-7b.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| sillyrp-7b.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| sillyrp-7b.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| sillyrp-7b.Q4_0.gguf | Q4_0 | 3.83GB |
| sillyrp-7b.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| sillyrp-7b.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| sillyrp-7b.Q4_K.gguf | Q4_K | 4.07GB |
| sillyrp-7b.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| sillyrp-7b.Q4_1.gguf | Q4_1 | 4.24GB |
| sillyrp-7b.Q5_0.gguf | Q5_0 | 4.65GB |
| sillyrp-7b.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| sillyrp-7b.Q5_K.gguf | Q5_K | 4.78GB |
| sillyrp-7b.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| sillyrp-7b.Q5_1.gguf | Q5_1 | 5.07GB |
| sillyrp-7b.Q6_K.gguf | Q6_K | 5.53GB |
| sillyrp-7b.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: apache-2.0 language:
- en tags:
- not-for-all-audiences
Silly RP 7B
This is one of my first merges, just throwing random things at the wall to see what works. Seems really good in my limited testing, but I can't make any promisses of it's quality. Just try it yourself and give me feedback I guess, I am rather intrested to see what others think about it. I don't know what I am doing, I don't even know benifits of diffrent merge methods :P
General setting guide (will update over time, share your settings please):
- Chat format ChatML (all the models merged are ChatML)
- Needs high repetition penalty after a while, starts out good but slowly gets into a loop without it
base_model:
- tavtav/eros-7b-test
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
- maywell/Synatra-7B-v0.3-RP
- NeverSleep/Noromaid-7B-0.4-DPO
- cogbuji/Mr-Grammatology-clinical-problems-Mistral-7B-0.5 library_name: transformers tags:
- mergekit
- merge
output
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the task arithmetic merge method using NeverSleep/Noromaid-7B-0.4-DPO as a base.
Models Merged
The following models were included in the merge:
- tavtav/eros-7b-test
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
- maywell/Synatra-7B-v0.3-RP
- cogbuji/Mr-Grammatology-clinical-problems-Mistral-7B-0.5
Configuration
The following YAML configuration was used to produce this model:
base_model: NeverSleep/Noromaid-7B-0.4-DPO
models:
- model: maywell/Synatra-7B-v0.3-RP
parameters:
weight: 0.2
- model: tavtav/eros-7b-test
parameters:
weight: 0.2
- model: cogbuji/Mr-Grammatology-clinical-problems-Mistral-7B-0.5
parameters:
weight: 0.2
- model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
parameters:
weight: 0.2
merge_method: task_arithmetic
parameters:
weight: 0.17
dtype: float16
random_seed: 694201337567099116663322537
- Created
- Sep 9, 2024
- Updated
- Sep 9, 2024