Instructions to use liuliu96/git-base-pokemon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liuliu96/git-base-pokemon with Transformers:

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

pipe = pipeline("image-text-to-text", model="liuliu96/git-base-pokemon")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("liuliu96/git-base-pokemon")
model = AutoModelForImageTextToText.from_pretrained("liuliu96/git-base-pokemon")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use liuliu96/git-base-pokemon with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liuliu96/git-base-pokemon"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liuliu96/git-base-pokemon",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/liuliu96/git-base-pokemon

SGLang

How to use liuliu96/git-base-pokemon 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 "liuliu96/git-base-pokemon" \
    --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": "liuliu96/git-base-pokemon",
		"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 "liuliu96/git-base-pokemon" \
        --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": "liuliu96/git-base-pokemon",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use liuliu96/git-base-pokemon with Docker Model Runner:
```
docker model run hf.co/liuliu96/git-base-pokemon
```

git-base-pokemon

This model is a fine-tuned version of microsoft/git-base on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0392
Wer Score: 2.4636

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
7.334	4.17	50	4.5690	13.9068
2.4021	8.33	100	0.4880	9.8480
0.1468	12.5	150	0.0350	0.4074
0.0179	16.67	200	0.0330	2.5888
0.006	20.83	250	0.0355	3.7037
0.0024	25.0	300	0.0373	4.7152
0.0017	29.17	350	0.0377	3.8314
0.0014	33.33	400	0.0385	3.2516
0.0012	37.5	450	0.0387	3.1609
0.0011	41.67	500	0.0390	2.6105
0.0011	45.83	550	0.0391	2.7650
0.0011	50.0	600	0.0392	2.4636

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3

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