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See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-RPMerge
Quantized with default exl2 quantization, still investigating the benefits/drawbacks of long context (32K) quantization.
This model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
# No parameters necessary for base model
- model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
#Emphasize the beginning of Vicuna format models
parameters:
weight: 0.19
density: 0.59
- model: /home/alpha/Models/Raw/Nous-Capybara-34B
parameters:
weight: 0.19
density: 0.55
# Vicuna format
- model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
parameters:
weight: 0.05
density: 0.55
- model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
parameters:
weight: 0.19
density: 0.55
- model: /home/alpha/Models/Raw/admo_limarp
parameters:
weight: 0.19
density: 0.48
- model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
parameters:
weight: 0.19
density: 0.59
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16