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

Libraries

How to use minwook/git-base-cartoon with Transformers:

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

pipe = pipeline("image-text-to-text", model="minwook/git-base-cartoon")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("minwook/git-base-cartoon")
model = AutoModelForMultimodalLM.from_pretrained("minwook/git-base-cartoon")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use minwook/git-base-cartoon with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/minwook/git-base-cartoon

SGLang

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

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

minwook commited on Aug 2, 2024

Commit

d234156

verified ·

1 Parent(s): ec3e0a8

Model save

Browse files

Files changed (4) hide show

README.md +4 -8
model.safetensors +1 -1
runs/Aug02_04-42-18_fe738dc76da4/events.out.tfevents.1722573748.fe738dc76da4.285.0 +2 -2
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -15,8 +15,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0646
-- Bleu Score: 0.0501
 ## Model description
@@ -43,18 +43,14 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 16
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 10
 - mixed_precision_training: Native AMP
 ### Training results
 | Training Loss | Epoch  | Step | Validation Loss | Bleu Score |
 |:-------------:|:------:|:----:|:---------------:|:----------:|
-| 7.4395        | 1.7544 | 50   | 4.7486          | 0.0011     |
-| 2.8193        | 3.5088 | 100  | 0.9704          | 0.0094     |
-| 0.4044        | 5.2632 | 150  | 0.1138          | 0.0588     |
-| 0.0733        | 7.0175 | 200  | 0.0661          | 0.0438     |
-| 0.0434        | 8.7719 | 250  | 0.0646          | 0.0501     |
 ### Framework versions

 This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 6.4422
+- Bleu Score: 0.0
 ## Model description
 - total_train_batch_size: 16
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 2
 - mixed_precision_training: Native AMP
 ### Training results
 | Training Loss | Epoch  | Step | Validation Loss | Bleu Score |
 |:-------------:|:------:|:----:|:---------------:|:----------:|
+| 7.925         | 1.7544 | 50   | 6.4422          | 0.0        |
 ### Framework versions

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