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

Libraries

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

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

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

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

git-base-pokemon

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

Loss: 0.0627
Wer Score: 8.5567

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
2.2863	2.1277	50	0.3771	0.4680
0.1088	4.2553	100	0.0445	0.4631
0.0219	6.3830	150	0.0438	0.4483
0.0152	8.5106	200	0.0437	0.4532
0.0124	10.6383	250	0.0474	0.4877
0.0101	12.7660	300	0.0499	2.7241
0.008	14.8936	350	0.0512	4.0493
0.0064	17.0213	400	0.0535	5.2857
0.0039	19.1489	450	0.0574	7.3103
0.0025	21.2766	500	0.0587	7.6847
0.0015	23.4043	550	0.0620	8.0443
0.0011	25.5319	600	0.0617	9.0788
0.0009	27.6596	650	0.0627	8.5567

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1

Downloads last month: 7

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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Model tree for isabelkim/git-base-pokemon

Base model

microsoft/git-base

Finetuned

(117)

this model