llama-8b-wiki10-31k-adapt-multilabel-classify

deb101/llama-8b-wiki10-31k-adapt-multilabel-classify

llama-8b-wiki10-31k-adapt-multilabel-classify is a text generation model from deb101. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama3.1.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Generation
Library: transformers
License: llama3.1
Model type: llama
Access: Public
Created: Aug 2, 2026
Updated: Aug 3, 2026
SHA: 3b722c24

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Base models

meta-llama/Llama-3.1-8B-Instruct

Model card

From the Hugging Face model README

llama-8b-wiki10-31k-adapt-multilabel-classify

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the None dataset. It achieves the following results on the evaluation set:

F1 Micro: 0.0057
F1 Macro: 0.0005
Precision At 5: 0.0146
Recall At 5: 0.0038
Psp At 5: 0.0048
Precision At 8: 0.0099
Recall At 8: 0.0041
Psp At 8: 0.0040
Precision At 15: 0.0062
Recall At 15: 0.0047
Psp At 15: 0.0037
Precision At 25: 0.0043
Recall At 25: 0.0055
Psp At 25: 0.0039
Rare F1 Micro: 0.0
Rare F1 Macro: 0.0
Rare Precision: 0.0
Rare Recall: 0.0
Rare Precision At 5: 0.0001
Rare Recall At 5: 0.0004
Rare Precision At 8: 0.0001
Rare Recall At 8: 0.0005
Rare Precision At 15: 0.0001
Rare Recall At 15: 0.0006
Rare Precision At 25: 0.0001
Rare Recall At 25: 0.0008
Not Rare F1 Micro: 0.0060
Not Rare F1 Macro: 0.0008
Not Rare Precision: 0.5636
Not Rare Recall: 0.0030
Not Rare Precision At 5: 0.0147
Not Rare Recall At 5: 0.0040
Not Rare Precision At 8: 0.0102
Not Rare Recall At 8: 0.0044
Not Rare Precision At 15: 0.0059
Not Rare Recall At 15: 0.0048
Not Rare Precision At 25: 0.0037
Not Rare Recall At 25: 0.0050
Loss: -3.3437

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
num_epochs: 3

Training results

Training Loss	Epoch	Step	F1 Micro	F1 Macro	Precision At 5	Recall At 5	Psp At 5	Precision At 8	Recall At 8	Psp At 8	Precision At 15	Recall At 15	Psp At 15	Precision At 25	Recall At 25	Psp At 25	Rare F1 Micro	Rare F1 Macro	Rare Precision	Rare Recall	Rare Precision At 5	Rare Recall At 5	Rare Precision At 8	Rare Recall At 8	Rare Precision At 15	Rare Recall At 15	Rare Precision At 25	Rare Recall At 25	Not Rare F1 Micro	Not Rare F1 Macro	Not Rare Precision	Not Rare Recall	Not Rare Precision At 5	Not Rare Recall At 5	Not Rare Precision At 8	Not Rare Recall At 8	Not Rare Precision At 15	Not Rare Recall At 15	Not Rare Precision At 25	Not Rare Recall At 25	Validation Loss
-3.5077	1.0	10850	0.1363	0.0081	0.4242	0.1163	0.1328	0.3148	0.1359	0.1194	0.1935	0.1539	0.1045	0.1237	0.1625	0.0986	0.0038	0.0010	0.0348	0.0020	0.0028	0.0073	0.0018	0.0076	0.0010	0.0079	0.0006	0.0083	0.1439	0.0129	0.5919	0.0819	0.4252	0.1240	0.3157	0.1448	0.1935	0.1633	0.1231	0.1715	-3.3029
-4.2213	2.0	21700	0.0247	0.0014	0.0708	0.0181	0.0211	0.0465	0.0189	0.0166	0.0262	0.0199	0.0132	0.0164	0.0208	0.0124	0.0000	0.0001	0.0312	0.0000	0.0002	0.0006	0.0001	0.0006	0.0001	0.0007	0.0001	0.0010	0.0262	0.0023	0.7033	0.0134	0.0710	0.0192	0.0467	0.0201	0.0259	0.0208	0.0158	0.0212	-3.3665
-4.3344	2.9998	32547	0.0057	0.0005	0.0146	0.0038	0.0048	0.0099	0.0041	0.0040	0.0062	0.0047	0.0037	0.0043	0.0055	0.0039	0.0	0.0	0.0	0.0	0.0001	0.0004	0.0001	0.0005	0.0001	0.0006	0.0001	0.0008	0.0060	0.0008	0.5636	0.0030	0.0147	0.0040	0.0102	0.0044	0.0059	0.0048	0.0037	0.0050	-3.3437

Framework versions

Transformers 4.49.0
Pytorch 2.7.0
Datasets 5.0.1
Tokenizers 0.21.4

Task: Text Generation
Library: transformers
Type: llama
License: llama3.1
Created: Aug 2, 2026
Updated: Aug 3, 2026