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

Libraries

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

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

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

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

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

SGLang

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

Docker Model Runner
How to use henriquepxl/git-base-pokemon with Docker Model Runner:
```
docker model run hf.co/henriquepxl/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: 0.3785
Wer Score: 2.7939

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: 10
eval_batch_size: 10
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 40
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
6.818	4.4444	50	4.1955	4.1407
2.3051	8.8889	100	0.6527	2.5893
0.2976	13.3333	150	0.2576	2.2313
0.1103	17.7778	200	0.2680	2.9372
0.056	22.2222	250	0.3025	2.8351
0.0323	26.6667	300	0.3286	2.9704
0.0207	31.1111	350	0.3488	2.9758
0.0144	35.5556	400	0.3587	2.8506
0.0111	40.0	450	0.3688	2.7654
0.0091	44.4444	500	0.3762	2.8455
0.0081	48.8889	550	0.3785	2.7939

Framework versions

Transformers 4.42.0.dev0
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

F32

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Image-Text-to-Text

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Model tree for henriquepxl/git-base-pokemon

Base model

microsoft/git-base

Finetuned

(117)

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