soikit_-_class_stand_model-exl2

Quantization made by Richard Erkhov.

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class_stand_model - EXL2

• Model creator: https://huggingface.co/soikit/
• Original model: https://huggingface.co/soikit/class_stand_model/

Available sizes

| Branch | Bits | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ | | 8_0 | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | 6_5 | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, recommended. | | 5_0 | 5.0 | Slightly lower quality vs 6.5, but usable on 8GB cards. | | 4_25 | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. | | 3_5 | 3.5 | Lower quality, only use if you have to. |

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/soikit_-_class_stand_model-exl2 class_stand_model-6_5

With huggingface hub:

pip3 install huggingface-hub

To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch: Linux:

huggingface-cli download soikit_-_class_stand_model-exl2 --revision 6_5 --local-dir class_stand_model-6_5 --local-dir-use-symlinks False

Windows (which apparently doesn't like _ in folders sometimes?):

huggingface-cli download soikit_-_class_stand_model-exl2 --revision 6_5 --local-dir class_stand_model-6.5 --local-dir-use-symlinks False

Original model description:

library_name: transformers tags:

• generated_from_trainer model-index:
• name: class_stand_model results: []

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<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

class_stand_model

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.6650

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-06
• train_batch_size: 2
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.46.1
• Pytorch 2.3.0+cu121
• Datasets 3.1.0
• Tokenizers 0.20.1