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Fulcrum-AI_-_Ryze-Nemo-gguf
RichardErkhov/Fulcrum-AI_-_Ryze-Nemo-gguf
Fulcrum-AI_-_Ryze-Nemo-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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116 GB
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.gguf135 GB · 100%
At a glance
- Access
- Public
- Created
- Aug 7, 2024
- Updated
- Aug 7, 2024
- SHA
- 54280375
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Ryze-Nemo - GGUF
- Model creator: https://huggingface.co/Fulcrum-AI/
- Original model: https://huggingface.co/Fulcrum-AI/Ryze-Nemo/
| Name | Quant method | Size |
|---|---|---|
| Ryze-Nemo.Q2_K.gguf | Q2_K | 3.71GB |
| Ryze-Nemo.IQ3_XS.gguf | IQ3_XS | 4.11GB |
| Ryze-Nemo.IQ3_S.gguf | IQ3_S | 4.3GB |
| Ryze-Nemo.Q3_K_S.gguf | Q3_K_S | 4.28GB |
| Ryze-Nemo.IQ3_M.gguf | IQ3_M | 4.42GB |
| Ryze-Nemo.Q3_K.gguf | Q3_K | 4.69GB |
| Ryze-Nemo.Q3_K_M.gguf | Q3_K_M | 4.69GB |
| Ryze-Nemo.Q3_K_L.gguf | Q3_K_L | 5.05GB |
| Ryze-Nemo.IQ4_XS.gguf | IQ4_XS | 5.24GB |
| Ryze-Nemo.Q4_0.gguf | Q4_0 | 5.44GB |
| Ryze-Nemo.IQ4_NL.gguf | IQ4_NL | 5.5GB |
| Ryze-Nemo.Q4_K_S.gguf | Q4_K_S | 5.48GB |
| Ryze-Nemo.Q4_K.gguf | Q4_K | 5.75GB |
| Ryze-Nemo.Q4_K_M.gguf | Q4_K_M | 5.75GB |
| Ryze-Nemo.Q4_1.gguf | Q4_1 | 5.99GB |
| Ryze-Nemo.Q5_0.gguf | Q5_0 | 6.54GB |
| Ryze-Nemo.Q5_K_S.gguf | Q5_K_S | 6.54GB |
| Ryze-Nemo.Q5_K.gguf | Q5_K | 6.69GB |
| Ryze-Nemo.Q5_K_M.gguf | Q5_K_M | 6.69GB |
| Ryze-Nemo.Q5_1.gguf | Q5_1 | 7.08GB |
| Ryze-Nemo.Q6_K.gguf | Q6_K | 7.7GB |
| Ryze-Nemo.Q8_0.gguf | Q8_0 | 9.97GB |
Original model description:
base_model:
- mistralai/Mistral-Nemo-Instruct-2407
- mistralai/Mistral-Nemo-Base-2407 library_name: transformers tags:
- mergekit
- merge
merged
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: mistralai/Mistral-Nemo-Instruct-2407
dtype: bfloat16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, 32]
model: mistralai/Mistral-Nemo-Base-2407
- Created
- Aug 7, 2024
- Updated
- Aug 7, 2024