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tssst/Nemesia-Qwen-2.5-7B-v1.0
Nemesia-Qwen-2.5-7B-v1.0 is a text generation model from tssst. Use it when you need the model to write or continue text. It is set up for transformers.
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From the Hugging Face model README

I don't know if its an issue with me or the model, but I can't seem to make quants of this model. I consistently get
llama_model_quantize: failed to quantize: tensor 'blk.24.attn_norm.weight' has invalid data. My whole setup has so
many stds and idiosynchasies that it may just be my system, but I tried redoing the whole thing and same stuff happened.
At this point it may be an issue with the NuSLERP method or one of the models I'm using in the merge. Not sure gang. I will
try swapping out a model or two in the merge and trying again to upload as a v2.0.
I tried it four more times, swapping out models, swapping base models and models entirely, swapping params,
git pulling llamacpp and mergekit, nothing. Errors when making quants, every one. I'm declaring this a lost cause.
I'm leaving this up in case someone gets it working.
Merge using the brand new NuSLERP method. Fresh out of the oven. Performance not guaranteed.
Uses the slightly-unstable EVA and two other finetunes I found. I also turned on both the NuSLERP exclusive mergekit options for fun.
Named after the nemesia, a temperate shrubby flower. I tried to pick a flower that sounded kind of like NuSLERP. It doesn't, but the name still has the '''essence''' of NuSLERP I guess? (it doesn't.) Very pretty flower nonetheless
This is a merge of pre-trained language models created using mergekit.
This model was merged using the NuSLERP merge method using EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: allura-org/Teleut-7b
parameters:
weight: 0.6
- model: FourOhFour/Vapor_v2_7B
parameters:
weight: 0.2
- model: EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1
parameters:
weight: 1.0
merge_method: nuslerp
base_model: EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1
parameters:
normalize: true
int8_mask: true
nuslerp_flatten: false
nuslerp_row_wise: true
dtype: float16