Instructions to use skillshikshyalearning/nepali_caption_model_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skillshikshyalearning/nepali_caption_model_output with Transformers:

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

pipe = pipeline("image-text-to-text", model="skillshikshyalearning/nepali_caption_model_output")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("skillshikshyalearning/nepali_caption_model_output")
model = AutoModelForMultimodalLM.from_pretrained("skillshikshyalearning/nepali_caption_model_output", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use skillshikshyalearning/nepali_caption_model_output with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/skillshikshyalearning/nepali_caption_model_output

SGLang

How to use skillshikshyalearning/nepali_caption_model_output 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 "skillshikshyalearning/nepali_caption_model_output" \
    --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": "skillshikshyalearning/nepali_caption_model_output",
		"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 "skillshikshyalearning/nepali_caption_model_output" \
        --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": "skillshikshyalearning/nepali_caption_model_output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use skillshikshyalearning/nepali_caption_model_output with Docker Model Runner:
```
docker model run hf.co/skillshikshyalearning/nepali_caption_model_output
```

nepali_caption_model_output

This model is a fine-tuned version of Salesforce/blip-image-captioning-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.4582

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: 16
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.8782	1.0	57	2.9302
1.4809	2.0	114	1.3804
1.0433	3.0	171	1.1510
0.9109	4.0	228	1.1120
0.8338	5.0	285	1.0309
0.7632	6.0	342	0.9980
0.6746	7.0	399	0.9841
0.6115	8.0	456	0.9942
0.5617	9.0	513	1.0004
0.4978	10.0	570	1.0198
0.4213	11.0	627	1.0671
0.3362	12.0	684	1.0902
0.2633	13.0	741	1.1330
0.2010	14.0	798	1.2048
0.1573	15.0	855	1.2534
0.1069	16.0	912	1.3084
0.0867	17.0	969	1.3298
0.0652	18.0	1026	1.3948
0.0454	19.0	1083	1.3941
0.0334	20.0	1140	1.4230
0.0240	21.0	1197	1.4377
0.0220	22.0	1254	1.4442
0.0181	23.0	1311	1.4515
0.0159	24.0	1368	1.4564
0.0158	25.0	1425	1.4582

Framework versions

Transformers 5.13.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: -

Safetensors

Model size

0.2B params

Tensor type

F32

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Image-Text-to-Text

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Model tree for skillshikshyalearning/nepali_caption_model_output

Base model

Salesforce/blip-image-captioning-base

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