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

Libraries

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

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

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

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("karlkwon/git-base-pokemon")
model = AutoModelForMultimodalLM.from_pretrained("karlkwon/git-base-pokemon")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use karlkwon/git-base-pokemon with Docker Model Runner:
```
docker model run hf.co/karlkwon/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: 0.0340
Wer Score: 2.1498

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: 10
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

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
7.321	2.13	50	4.4679	21.5557
2.2294	4.26	100	0.3441	11.8745
0.1021	6.38	150	0.0283	0.5672
0.0187	8.51	200	0.0251	0.6018
0.0086	10.64	250	0.0272	3.6786
0.0038	12.77	300	0.0288	6.7119
0.0019	14.89	350	0.0300	4.2023
0.0011	17.02	400	0.0308	4.0768
0.0009	19.15	450	0.0310	3.5980
0.0007	21.28	500	0.0315	3.5723
0.0007	23.4	550	0.0323	2.8835
0.0006	25.53	600	0.0325	2.8399
0.0006	27.66	650	0.0330	2.6274
0.0006	29.79	700	0.0331	2.5416
0.0006	31.91	750	0.0334	2.4213
0.0006	34.04	800	0.0335	2.3214
0.0006	36.17	850	0.0330	2.2330
0.0006	38.3	900	0.0337	2.2254
0.0006	40.43	950	0.0338	2.1652
0.0006	42.55	1000	0.0340	2.1447
0.0006	44.68	1050	0.0340	2.1767
0.0006	46.81	1100	0.0340	2.1536
0.0006	48.94	1150	0.0340	2.1498

Framework versions

Transformers 4.30.2
Pytorch 2.0.0
Datasets 2.12.0
Tokenizers 0.13.3

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