Instructions to use kavinh07/nid-ocr-vit-mbart50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kavinh07/nid-ocr-vit-mbart50 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="kavinh07/nid-ocr-vit-mbart50")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("kavinh07/nid-ocr-vit-mbart50")
model = AutoModelForImageTextToText.from_pretrained("kavinh07/nid-ocr-vit-mbart50")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kavinh07/nid-ocr-vit-mbart50 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kavinh07/nid-ocr-vit-mbart50"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kavinh07/nid-ocr-vit-mbart50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kavinh07/nid-ocr-vit-mbart50

SGLang

How to use kavinh07/nid-ocr-vit-mbart50 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kavinh07/nid-ocr-vit-mbart50" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kavinh07/nid-ocr-vit-mbart50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kavinh07/nid-ocr-vit-mbart50" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kavinh07/nid-ocr-vit-mbart50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kavinh07/nid-ocr-vit-mbart50 with Docker Model Runner:
```
docker model run hf.co/kavinh07/nid-ocr-vit-mbart50
```

nid-ocr-vit-mbart50

This model is a fine-tuned version of kavinh07/nid-ocr-vit-mbart50 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.0364
Cer: 0.4548
Wer: 0.9174

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: 1e-06
train_batch_size: 8
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 500
num_epochs: 1000

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
No log	0	0	1.2303	15.3671	1.3915
9.2504	0.1616	500	0.9696	10.2246	1.0
8.0005	0.3232	1000	0.9036	8.8354	1.0011
5.7473	0.4848	1500	0.9035	6.2680	1.0069
5.5502	0.6465	2000	0.9012	6.0817	0.9986
5.4428	0.8081	2500	0.8650	5.9736	0.9996
5.3589	0.9697	3000	0.8459	5.8645	1.0078
5.251	1.1312	3500	0.8404	5.7812	0.9977
5.1871	1.2928	4000	0.8297	5.7049	1.0089
5.1285	1.4545	4500	0.8291	5.6321	1.0114
5.046	1.6161	5000	0.8213	5.5513	1.0032
4.9711	1.7777	5500	0.8038	5.4780	1.0153
4.9327	1.9393	6000	0.8212	5.3911	1.0060
4.8355	2.1008	6500	0.7972	5.3168	1.0098
4.7741	2.2625	7000	0.8124	5.2250	1.0284
4.7183	2.4241	7500	0.7922	5.1361	1.0355
4.6461	2.5857	8000	0.7886	5.0547	1.0044
4.5577	2.7473	8500	0.7833	4.9441	1.0339
4.4995	2.9089	9000	0.7695	4.8550	1.0371
4.4065	3.0705	9500	0.7786	4.7699	1.0181
4.3524	3.2321	10000	0.7639	4.6997	1.0144
4.2801	3.3937	10500	0.7654	4.6295	1.0304
4.2108	3.5553	11000	0.7430	4.5599	1.0158
4.1802	3.7169	11500	0.7531	4.4809	1.0334
4.1174	3.8785	12000	0.7593	4.4223	1.0313
4.0412	4.0401	12500	0.7556	4.3625	1.0217
4.0191	4.2017	13000	0.7370	4.2943	1.0368
3.9507	4.3633	13500	0.7457	4.2372	1.0233
3.9229	4.5249	14000	0.7285	4.1910	1.0172
3.8619	4.6865	14500	0.7197	4.1271	1.0298
3.8147	4.8482	15000	0.7441	4.0904	1.0465
3.7776	5.0097	15500	0.7282	4.0349	1.0593
3.7118	5.1713	16000	0.7112	3.9809	1.0282
3.6925	5.3329	16500	0.7621	3.9965	1.0126
3.6791	5.4945	17000	0.6975	3.9024	1.0256
3.6085	5.6562	17500	0.6918	3.8385	1.0107
3.5786	5.8178	18000	0.6998	3.8379	0.9911
3.5533	5.9794	18500	0.6789	3.7665	1.0430
3.4947	6.1409	19000	0.6672	3.7340	1.0062
3.4588	6.3025	19500	0.6819	3.6823	1.0343
3.4437	6.4642	20000	0.6899	3.6471	1.0508
3.3894	6.6258	20500	0.6729	3.6058	1.0147
3.374	6.7874	21000	0.6568	3.6300	0.9968
3.3134	6.9490	21500	0.6528	3.5288	1.0266
3.2998	7.1105	22000	0.6610	3.5018	1.0293
3.2619	7.2722	22500	0.6536	3.4825	1.0149
3.2441	7.4338	23000	0.6479	3.4272	1.0021
3.2086	7.5954	23500	0.6679	3.4185	1.0330
3.1562	7.7570	24000	0.6373	3.3748	1.0162
3.1216	7.9186	24500	0.6167	3.3510	0.9824
3.095	8.0802	25000	0.6345	3.3077	1.0137
3.0623	8.2418	25500	0.6337	3.2836	1.0043
3.0552	8.4034	26000	0.6235	3.2377	1.0199
3.0272	8.5650	26500	0.6337	3.2284	1.0140
3.0418	8.7266	27000	0.6151	3.1740	0.9988
2.9824	8.8882	27500	0.6140	3.1549	1.0162
2.9227	9.0498	28000	0.6190	3.1212	1.0048
2.9161	9.2114	28500	0.6172	3.0897	0.9972
2.895	9.3730	29000	0.6128	3.0622	1.0167
2.8645	9.5346	29500	0.6088	3.0525	1.0126
2.8518	9.6962	30000	0.6040	3.0412	1.0178
2.8416	9.8579	30500	0.6036	3.0067	0.9936
2.7986	10.0194	31000	0.6022	2.9544	1.0115
2.7933	10.1810	31500	0.5777	2.9723	0.9828
2.7627	10.3426	32000	0.5906	2.9261	0.9991
2.7305	10.5042	32500	0.6061	2.9096	1.0400
2.7207	10.6659	33000	0.5854	2.8785	1.0217
2.6922	10.8275	33500	0.5790	2.8620	1.0089
2.6805	10.9891	34000	0.5811	2.8462	1.0107
2.665	11.1506	34500	0.5794	2.9425	1.0190
2.6275	11.3122	35000	0.5802	2.8207	0.9901
2.6015	11.4739	35500	0.5726	2.8202	1.0069
2.5915	11.6355	36000	0.5691	2.7658	1.0014
2.596	11.7971	36500	0.5603	2.7660	0.9751
2.5609	11.9587	37000	0.5630	2.7346	0.9895
2.5313	12.1202	37500	0.5662	2.7191	0.9844
2.5209	12.2819	38000	0.5492	2.7193	0.9888
2.5039	12.4435	38500	0.5577	2.6819	1.0119
2.494	12.6051	39000	0.5643	2.7048	0.9805
2.4651	12.7667	39500	0.5409	2.6311	0.9735
2.452	12.9283	40000	0.5492	2.6221	0.9718
2.429	13.0899	40500	0.5505	2.6050	0.9673
2.3802	13.2515	41000	0.5380	2.5894	0.9766
2.389	13.4131	41500	0.5539	2.5650	0.9993
2.3934	13.5747	42000	0.5476	2.5836	1.0110
2.3941	13.7363	42500	0.5363	2.5230	0.9933
2.3596	13.8979	43000	0.5354	2.5024	1.0030
2.3465	14.0595	43500	0.5226	2.4961	0.9771
2.3063	14.2211	44000	0.5312	2.5007	0.9851
2.2892	14.3827	44500	0.5488	2.4938	1.0112
2.3054	14.5443	45000	0.5483	2.4543	0.9945
2.3007	14.7059	45500	0.5254	2.4403	0.9714
2.2859	14.8676	46000	0.5198	2.4379	0.9389
2.2455	15.0291	46500	0.5295	2.4459	0.9883
2.2289	15.1907	47000	0.5417	2.4485	1.0069
2.2374	15.3523	47500	0.5261	2.4002	0.9842
2.2152	15.5139	48000	0.5222	2.3864	0.9623
2.2035	15.6756	48500	0.5265	2.3684	0.9664
2.1989	15.8372	49000	0.5275	2.3414	0.9844
2.2085	15.9988	49500	0.5349	2.3795	0.9988
2.1766	16.1603	50000	0.5141	2.3293	0.9650
2.1555	16.3219	50500	0.5048	2.3384	0.9526
2.1463	16.4836	51000	0.5028	2.3157	0.9542
2.1207	16.6452	51500	0.5090	2.3008	0.9673
2.0862	16.8068	52000	0.5161	2.2767	0.9734
2.1035	16.9684	52500	0.4997	2.2525	0.9575
2.0845	17.1299	53000	0.5176	2.2653	0.9664
2.0481	17.2916	53500	0.5150	2.2575	0.9725
2.0818	17.4532	54000	0.5185	2.2501	0.9645
2.0494	17.6148	54500	0.5121	2.2913	0.9636
2.0594	17.7764	55000	0.5046	2.2314	0.9528
2.0228	17.9380	55500	0.5140	2.1898	0.9515
1.9942	18.0996	56000	0.5031	2.1815	0.9687
2.0066	18.2612	56500	0.5103	2.1726	0.9449
1.9916	18.4228	57000	0.5026	2.1621	0.9570
1.9776	18.5844	57500	0.4993	2.2092	0.9503
1.9872	18.7460	58000	0.4986	2.1589	0.9474
1.9727	18.9076	58500	0.5149	2.1635	0.9577
1.9535	19.0692	59000	0.4985	2.1328	0.9625
1.92	19.2308	59500	0.4968	2.1301	0.9575
1.94	19.3924	60000	0.5079	2.1076	0.9652
1.9291	19.5540	60500	0.4925	2.1098	0.9345
1.9162	19.7156	61000	0.5120	2.0985	0.9552
1.9257	19.8773	61500	0.5145	2.1106	0.9401
1.8887	20.0388	62000	0.4994	2.0634	0.9462
1.8955	20.2004	62500	0.5192	2.0742	0.9718
1.8662	20.3620	63000	0.4984	2.0643	0.9380
1.875	20.5236	63500	0.4983	2.0561	0.9551
1.8691	20.6853	64000	0.4946	2.0392	0.9419
1.8617	20.8469	64500	0.4898	2.0450	0.9417
1.8503	21.0084	65000	0.4917	2.0269	0.9698
1.8008	21.1700	65500	0.4884	2.0447	0.9494
1.8369	21.3316	66000	0.4967	2.0651	0.9803
1.8249	21.4933	66500	0.4853	2.0096	0.9504
1.8254	21.6549	67000	0.4857	2.0043	0.9439
1.8156	21.8165	67500	0.4874	2.0068	0.9561
1.8006	21.9781	68000	0.4845	1.9714	0.9231
1.7588	22.1396	68500	0.4786	1.9863	0.9158
1.7649	22.3013	69000	0.4906	1.9707	0.9538
1.7717	22.4629	69500	0.4723	1.9744	0.9361
1.7525	22.6245	70000	0.4845	1.9495	0.9419
1.7633	22.7861	70500	0.4840	1.9321	0.9332
1.7624	22.9477	71000	0.4918	1.9705	0.9355
1.7371	23.1093	71500	0.4884	1.9577	0.9526
1.7274	23.2709	72000	0.4757	1.9301	0.9352
1.7226	23.4325	72500	0.4747	2.0253	0.9174
1.7132	23.5941	73000	0.4864	1.9541	0.9380
1.7192	23.7557	73500	0.4990	1.9386	0.9274
1.7139	23.9173	74000	0.4891	1.9189	0.9449
1.6759	24.0789	74500	0.4823	1.9061	0.9469
1.6849	24.2405	75000	0.4871	1.8908	0.9616
1.6593	24.4021	75500	0.4863	1.9240	0.9313
1.666	24.5637	76000	0.4893	1.8830	0.9487
1.6789	24.7253	76500	0.4807	1.8743	0.9456
1.6831	24.8869	77000	0.4785	1.8504	0.9595
1.6278	25.0485	77500	0.4805	1.8496	0.9378
1.6317	25.2101	78000	0.4838	1.8459	0.9449
1.638	25.3717	78500	0.4831	1.8526	0.9369
1.6308	25.5333	79000	0.4876	1.8431	0.9380
1.6276	25.6949	79500	0.4713	1.8340	0.9272
1.6386	25.8566	80000	0.4871	1.8355	0.9449
1.6348	26.0181	80500	0.4826	1.8633	0.9563
1.5995	26.1797	81000	0.4713	1.8172	0.9254
1.6034	26.3413	81500	0.4754	1.8213	0.9218
1.5863	26.5029	82000	0.4774	1.8002	0.9416
1.5766	26.6646	82500	0.4762	1.7909	0.9245
1.5993	26.8262	83000	0.4838	1.8065	0.9355
1.565	26.9878	83500	0.4685	1.7828	0.9277
1.546	27.1493	84000	0.4650	1.7945	0.9329
1.5652	27.3109	84500	0.4742	1.7707	0.9410
1.5414	27.4726	85000	0.4750	1.7866	0.9162
1.5618	27.6342	85500	0.4713	1.7592	0.9382
1.5583	27.7958	86000	0.4725	1.7506	0.9350
1.5598	27.9574	86500	0.4793	1.7576	0.9552
1.5363	28.1189	87000	0.4816	1.7699	0.9648
1.5212	28.2806	87500	0.4700	1.7569	0.9437
1.5206	28.4422	88000	0.4727	1.7346	0.9345
1.5104	28.6038	88500	0.4655	1.7395	0.9124
1.5163	28.7654	89000	0.4694	1.7348	0.9291
1.5089	28.9270	89500	0.4749	1.7344	0.9380
1.4757	29.0886	90000	0.4738	1.7561	0.9229
1.4853	29.2502	90500	0.4691	1.7237	0.9183
1.5013	29.4118	91000	0.4691	1.7095	0.9206
1.484	29.5734	91500	0.4677	1.7125	0.9378
1.486	29.7350	92000	0.4687	1.7058	0.9348
1.4854	29.8966	92500	0.4589	1.7008	0.9325
1.4496	30.0582	93000	0.4738	1.6858	0.9480
1.4583	30.2198	93500	0.4673	1.6989	0.9336
1.453	30.3814	94000	0.4590	1.7015	0.9352
1.4612	30.5430	94500	0.4732	1.6864	0.9456
1.4551	30.7046	95000	0.4719	1.6780	0.9416
1.4404	30.8663	95500	0.4733	1.6837	0.9412
1.4318	31.0278	96000	0.4669	1.6688	0.9387
1.4208	31.1894	96500	0.4634	1.6800	0.9307
1.4164	31.3510	97000	0.4726	1.6698	0.9380
1.4292	31.5126	97500	0.4684	1.6749	0.9316
1.4158	31.6743	98000	0.4645	1.6680	0.9165
1.4044	31.8359	98500	0.4653	1.6848	0.9499
1.4157	31.9975	99000	0.4657	1.6649	0.9142
1.4024	32.1590	99500	0.4717	1.6544	0.9277
1.3878	32.3206	100000	0.4753	1.6490	0.9298
1.4004	32.4823	100500	0.4682	1.6389	0.9318
1.3856	32.6439	101000	0.4655	1.6438	0.9440
1.3882	32.8055	101500	0.4758	1.6354	0.9384
1.386	32.9671	102000	0.4587	1.6524	0.9075
1.3725	33.1286	102500	0.4584	1.6229	0.9201
1.3664	33.2903	103000	0.4735	1.6094	0.9426
1.3683	33.4519	103500	0.4647	1.6160	0.9325
1.3476	33.6135	104000	0.4692	1.6143	0.9327
1.3368	33.7751	104500	0.4599	1.6105	0.9247
1.3613	33.9367	105000	0.4685	1.6047	0.9316
1.34	34.0983	105500	0.4691	1.5975	0.9359
1.3441	34.2599	106000	0.4673	1.5877	0.9380
1.3553	34.4215	106500	0.4732	1.5935	0.9142
1.3252	34.5831	107000	0.4665	1.5932	0.9284
1.3297	34.7447	107500	0.4747	1.5848	0.9382
1.3392	34.9063	108000	0.4534	1.5840	0.9137
1.2923	35.0679	108500	0.4574	1.5747	0.9126
1.3337	35.2295	109000	0.4664	1.5981	0.9281
1.3046	35.3911	109500	0.4648	1.5864	0.9350
1.3127	35.5527	110000	0.4587	1.5820	0.9181
1.3112	35.7143	110500	0.4615	1.5767	0.9169
1.3136	35.8760	111000	0.4579	1.5645	0.9323
1.287	36.0375	111500	0.4485	1.5979	0.9215
1.2924	36.1991	112000	0.4540	1.6108	0.9245
1.2649	36.3607	112500	0.4572	1.5457	0.9197
1.2876	36.5223	113000	0.4617	1.5497	0.9316
1.2749	36.6840	113500	0.4611	1.5531	0.9139
1.2789	36.8456	114000	0.4686	1.5476	0.9481
1.2762	37.0071	114500	0.4615	1.5508	0.9295
1.2686	37.1687	115000	0.4694	1.5395	0.9155
1.2623	37.3303	115500	0.4594	1.5326	0.9368
1.2629	37.4920	116000	0.4521	1.5350	0.9160
1.2617	37.6536	116500	0.4551	1.5292	0.9224
1.2389	37.8152	117000	0.4546	1.5240	0.9147
1.2589	37.9768	117500	0.4492	1.5335	0.9220
1.2482	38.1383	118000	0.4640	1.5376	0.9208
1.221	38.3000	118500	0.4567	1.5205	0.9242
1.2259	38.4616	119000	0.4521	1.5148	0.9266
1.2298	38.6232	119500	0.4559	1.5160	0.9295
1.2223	38.7848	120000	0.4542	1.5196	0.9135
1.2268	38.9464	120500	0.4504	1.5072	0.9162
1.1967	39.1080	121000	0.4579	1.5069	0.9222
1.2055	39.2696	121500	0.4633	1.5016	0.9465
1.2166	39.4312	122000	0.4700	1.5283	0.9446
1.2001	39.5928	122500	0.4605	1.4916	0.9346
1.222	39.7544	123000	0.4517	1.5062	0.9190
1.2015	39.9160	123500	0.4556	1.4924	0.9252
1.1913	40.0776	124000	0.4668	1.4850	0.9414
1.1767	40.2392	124500	0.4554	1.4908	0.9410
1.1917	40.4008	125000	0.4691	1.4940	0.9380
1.198	40.5624	125500	0.4680	1.4781	0.9252
1.1973	40.7240	126000	0.4546	1.4773	0.9169
1.1854	40.8857	126500	0.4573	1.4679	0.9202
1.1508	41.0472	127000	0.4558	1.4699	0.9270
1.1576	41.2088	127500	0.4702	1.4966	0.9298
1.1576	41.2088	127500	0.4553	1.0844	0.9153
1.1698	40.9994	128000	0.4629	1.0873	0.9535
1.174	41.1595	128500	0.4373	1.0774	0.9252
1.1544	41.3197	129000	0.4444	1.0692	0.9286
1.1588	41.4798	129500	0.4398	1.0684	0.9352
1.1581	41.6400	130000	0.4258	1.0582	0.9236
1.1539	41.8001	130500	0.4421	1.0679	0.9269
1.1695	41.9603	131000	0.4474	1.0705	0.9518
1.1333	42.1204	131500	0.4458	1.0891	0.9252
1.1378	42.2806	132000	0.4186	1.0970	0.9120
1.1379	42.4407	132500	0.4428	1.0974	0.9302
1.1514	42.6009	133000	0.4246	1.0703	0.9336
1.1337	42.7611	133500	0.4421	1.0858	0.9302
1.1466	42.9212	134000	0.4343	1.0606	0.9203
1.1224	43.0814	134500	0.4500	1.0613	0.9352
1.1155	43.2415	135000	0.4272	1.0546	0.9070
1.1284	43.4017	135500	0.4389	1.0462	0.9120
1.1354	43.5618	136000	0.4325	1.0596	0.9103
1.1208	43.7220	136500	0.4364	1.0675	0.9153
1.1403	43.8821	137000	0.4373	1.0567	0.9186
1.1015	44.0423	137500	0.4408	1.0623	0.9203
1.1033	44.2024	138000	0.4401	1.0543	0.9153
1.1144	44.3626	138500	0.4454	1.0653	0.9153
1.1144	44.3626	138500	1.0034	0.4480	0.8893
1.0938	44.5227	139000	1.0132	0.4389	0.8645
1.0857	44.6829	139500	1.0531	0.4513	0.8909
1.0924	44.8430	140000	1.0047	0.4537	0.8942
1.0857	45.0032	140500	1.0283	0.4513	0.9008
1.0664	45.1634	141000	1.0230	0.4396	0.8595
1.0899	45.3235	141500	1.0045	0.4506	0.8810
1.0961	45.4837	142000	0.9996	0.4561	0.9107
1.0815	45.6438	142500	1.0168	0.4411	0.8942
1.0823	45.8040	143000	1.0278	0.4469	0.8727
1.0858	45.9641	143500	1.0309	0.4628	0.9273
1.0554	46.1243	144000	1.0313	0.4442	0.9074
1.0647	46.2844	144500	1.0232	0.4451	0.9174
1.0665	46.4446	145000	1.0170	0.4515	0.8893
1.0615	46.6047	145500	1.0106	0.4354	0.8744
1.0738	46.7649	146000	1.0478	0.4692	0.8992
1.0563	46.9250	146500	1.0242	0.4473	0.9107
1.0425	47.0852	147000	1.0364	0.4548	0.9174

Framework versions

Transformers 4.54.1
Pytorch 2.7.1+cu126
Datasets 4.5.0
Tokenizers 0.21.4

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Model size

0.8B params

Tensor type

F32

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