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

Libraries

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

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

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

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LinpengS/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": "LinpengS/git-base-pokemon",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use LinpengS/git-base-pokemon with Docker Model Runner:
```
docker model run hf.co/LinpengS/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: 0.0407
Wer Score: 2.1746

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: 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
7.3192	2.13	50	4.4908	21.3506
2.2486	4.26	100	0.3526	10.3803
0.1047	6.38	150	0.0321	0.3635
0.0222	8.51	200	0.0298	0.6636
0.0145	10.64	250	0.0286	2.1759
0.0081	12.77	300	0.0321	2.9690
0.004	14.89	350	0.0340	2.2962
0.002	17.02	400	0.0356	2.1837
0.0012	19.15	450	0.0370	3.1501
0.0009	21.28	500	0.0379	2.5821
0.0007	23.4	550	0.0382	2.7995
0.0006	25.53	600	0.0386	2.8318
0.0006	27.66	650	0.0387	2.4541
0.0006	29.79	700	0.0390	2.6404
0.0006	31.91	750	0.0395	2.5614
0.0006	34.04	800	0.0395	2.5317
0.0006	36.17	850	0.0399	2.5886
0.0006	38.3	900	0.0403	2.3829
0.0006	40.43	950	0.0404	2.2937
0.0006	42.55	1000	0.0404	2.2173
0.0006	44.68	1050	0.0406	2.1617
0.0006	46.81	1100	0.0406	2.1669
0.0006	48.94	1150	0.0407	2.1746

Framework versions

Transformers 4.27.4
Pytorch 1.13.1
Datasets 2.11.0
Tokenizers 0.13.3

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