Instructions to use deepakshirkem/image-description_to_emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepakshirkem/image-description_to_emotion with Transformers:

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

pipe = pipeline("text-generation", model="deepakshirkem/image-description_to_emotion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepakshirkem/image-description_to_emotion")
model = AutoModelForCausalLM.from_pretrained("deepakshirkem/image-description_to_emotion")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use deepakshirkem/image-description_to_emotion with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepakshirkem/image-description_to_emotion

SGLang

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

Docker Model Runner
How to use deepakshirkem/image-description_to_emotion with Docker Model Runner:
```
docker model run hf.co/deepakshirkem/image-description_to_emotion
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

image-description_to_emotion

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-360M on the arrow dataset. It achieves the following results on the evaluation set:

Loss: 0.1650

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: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.5979	0.3361	50	0.5039
0.3262	0.6723	100	0.2783
0.2599	1.0084	150	0.2305
0.2211	1.3445	200	0.2071
0.2004	1.6807	250	0.1969
0.2094	2.0168	300	0.1840
0.1788	2.3529	350	0.1797
0.1709	2.6891	400	0.1739
0.1604	3.0252	450	0.1693
0.141	3.3613	500	0.1671
0.1479	3.6975	550	0.1650

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for deepakshirkem/image-description_to_emotion

Base model

HuggingFaceTB/SmolLM2-360M

Finetuned

(101)

this model

Quantizations

1 model