Instructions to use guo1006/git-base-pokemon-captioning-generate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use guo1006/git-base-pokemon-captioning-generate with Transformers:

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

pipe = pipeline("image-text-to-text", model="guo1006/git-base-pokemon-captioning-generate")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("guo1006/git-base-pokemon-captioning-generate")
model = AutoModelForImageTextToText.from_pretrained("guo1006/git-base-pokemon-captioning-generate")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use guo1006/git-base-pokemon-captioning-generate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "guo1006/git-base-pokemon-captioning-generate"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "guo1006/git-base-pokemon-captioning-generate",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/guo1006/git-base-pokemon-captioning-generate

SGLang

How to use guo1006/git-base-pokemon-captioning-generate 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 "guo1006/git-base-pokemon-captioning-generate" \
    --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": "guo1006/git-base-pokemon-captioning-generate",
		"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 "guo1006/git-base-pokemon-captioning-generate" \
        --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": "guo1006/git-base-pokemon-captioning-generate",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use guo1006/git-base-pokemon-captioning-generate with Docker Model Runner:
```
docker model run hf.co/guo1006/git-base-pokemon-captioning-generate
```

git-base-pokemon-captioning-generate

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1551
Wer Score: 4.4399

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: 16
seed: 42
gradient_accumulation_steps: 2
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: linear
num_epochs: 40
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
4.6973	4.1702	100	0.4374	15.2827
0.1366	8.3404	200	0.1118	4.2933
0.0431	12.5106	300	0.1225	4.5509
0.0115	16.6809	400	0.1358	5.0092
0.003	20.8511	500	0.1445	5.2642
0.0015	25.0	600	0.1506	4.6816
0.0012	29.1702	700	0.1537	4.3778
0.0011	33.3404	800	0.1542	4.1532
0.001	37.5106	900	0.1552	4.3699

Framework versions

Transformers 4.56.1
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.22.0

Downloads last month: 7

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

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