unsloth-test

Treza12/unsloth-test

unsloth-test is a machine learning model from Treza12. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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

Downloads · 30 days

Access

Public

Updated Jan 22, 2024

Repo size

19.1 MB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors18.9 MB · 86%

At a glance

Access: Public
Created: Jan 22, 2024
Updated: Jan 22, 2024
SHA: a8b434cb

Base models

ybelkada/falcon-7b-sharded-bf16

Model card

From the Hugging Face model README

unsloth-test

This model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1783

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-05
train_batch_size: 2
eval_batch_size: 8
seed: 3407
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.9064	0.03	10	2.3285
1.7147	0.05	20	2.1141
1.9815	0.08	30	1.9912
1.6473	0.11	40	1.9045
1.8572	0.14	50	1.8468
1.4015	0.16	60	1.7899
1.3057	0.19	70	1.7523
1.4202	0.22	80	1.7084
1.2665	0.24	90	1.6817
1.1922	0.27	100	1.6452
1.0946	0.3	110	1.6227
1.1268	0.33	120	1.6033
1.4784	0.35	130	1.5873
1.2049	0.38	140	1.5733
1.1141	0.41	150	1.5400
1.2119	0.43	160	1.5317
1.5383	0.46	170	1.5195
1.308	0.49	180	1.5100
1.6075	0.52	190	1.4901
1.4443	0.54	200	1.4942
1.4493	0.57	210	1.4685
0.8844	0.6	220	1.4677
1.2082	0.62	230	1.4518
1.1994	0.65	240	1.4380
0.7472	0.68	250	1.4300
1.0516	0.71	260	1.4183
0.9373	0.73	270	1.4178
1.1221	0.76	280	1.4010
1.0978	0.79	290	1.3931
1.1696	0.82	300	1.3853
0.8722	0.84	310	1.3673
0.8707	0.87	320	1.3583
0.9933	0.9	330	1.3480
1.0988	0.92	340	1.3450
0.9995	0.95	350	1.3399
0.9277	0.98	360	1.3414
0.7538	1.01	370	1.3260
0.7933	1.03	380	1.3152
0.9362	1.06	390	1.3101
0.8345	1.09	400	1.3023
0.9271	1.11	410	1.3044
1.0953	1.14	420	1.3081
1.0863	1.17	430	1.2927
0.8999	1.2	440	1.2951
1.0146	1.22	450	1.2842
0.6808	1.25	460	1.2784
0.8005	1.28	470	1.2713
0.746	1.3	480	1.2642
0.9976	1.33	490	1.2661
0.7508	1.36	500	1.2539
0.8439	1.39	510	1.2462
0.7671	1.41	520	1.2465
0.7547	1.44	530	1.2421
0.6882	1.47	540	1.2426
1.0217	1.49	550	1.2381
0.6518	1.52	560	1.2324
0.9974	1.55	570	1.2324
0.7781	1.58	580	1.2292
0.7264	1.6	590	1.2296
0.9346	1.63	600	1.2255
0.7346	1.66	610	1.2223
0.9378	1.68	620	1.2164
0.8924	1.71	630	1.2176
1.2544	1.74	640	1.2157
0.7287	1.77	650	1.2155
0.7783	1.79	660	1.2069
0.8022	1.82	670	1.2049
0.9351	1.85	680	1.1969
0.891	1.88	690	1.2017
0.9973	1.9	700	1.2013
0.7192	1.93	710	1.2034
0.6488	1.96	720	1.1970
0.6643	1.98	730	1.1986
0.9502	2.01	740	1.1922
0.7626	2.04	750	1.1951
0.7211	2.07	760	1.1904
0.6385	2.09	770	1.1911
0.7917	2.12	780	1.1898
0.7153	2.15	790	1.1914
0.808	2.17	800	1.1869
0.6567	2.2	810	1.1870
0.5624	2.23	820	1.1909
0.7756	2.26	830	1.1869
0.8657	2.28	840	1.1864
1.0908	2.31	850	1.1866
0.9891	2.34	860	1.1835
0.7495	2.36	870	1.1784
0.7918	2.39	880	1.1809
0.8408	2.42	890	1.1808
0.9415	2.45	900	1.1807
0.8744	2.47	910	1.1793
0.6232	2.5	920	1.1784
0.7813	2.53	930	1.1797
0.959	2.55	940	1.1795
0.8303	2.58	950	1.1796
0.7675	2.61	960	1.1790
0.9452	2.64	970	1.1784
0.7509	2.66	980	1.1783
0.5698	2.69	990	1.1784
0.8727	2.72	1000	1.1783

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Created: Jan 22, 2024
Updated: Jan 22, 2024