Instructions to use srk0102200/AnimTOON-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use srk0102200/AnimTOON-3B with Transformers:

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

pipe = pipeline("text-generation", model="srk0102200/AnimTOON-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("srk0102200/AnimTOON-3B")
model = AutoModelForCausalLM.from_pretrained("srk0102200/AnimTOON-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use srk0102200/AnimTOON-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "srk0102200/AnimTOON-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srk0102200/AnimTOON-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/srk0102200/AnimTOON-3B

SGLang

How to use srk0102200/AnimTOON-3B 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 "srk0102200/AnimTOON-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srk0102200/AnimTOON-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "srk0102200/AnimTOON-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srk0102200/AnimTOON-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use srk0102200/AnimTOON-3B with Docker Model Runner:
```
docker model run hf.co/srk0102200/AnimTOON-3B
```

srk0102200 commited on Apr 10

Commit

f1ae30a

verified ·

1 Parent(s): 64b216c

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -5

README.md CHANGED Viewed

@@ -11,6 +11,8 @@ tags:
 - animtoon
 - vector-animation
 - text-to-animation
 datasets:
 - OmniLottie/MMLottie-2M
 pipeline_tag: text-generation
@@ -75,7 +77,7 @@ print(result)
 ## Convert to .lottie
 ```python
-# Clone: git clone https://github.com/srk0102/svg-animator.git
 import sys; sys.path.insert(0, 'src')
 from toon_animator import animtoon_to_dotlottie_full
@@ -97,7 +99,7 @@ lottie_dict = anim.to_dict()
 # Output: .lottie file with real SVG shapes + AI animations
 ```
-See full pipeline: [test_svg_pipeline.py](https://github.com/srk0102/svg-animator/blob/master/test_svg_pipeline.py)
 ## Benchmark Results (Measured)
@@ -156,7 +158,8 @@ OmniLottie generates everything in one model → hallucinated shapes, token bloa
 ## Links
-- **GitHub:** [github.com/srk0102/svg-animator](https://github.com/srk0102/svg-animator)
 - **OmniLottie (comparison):** [arxiv.org/abs/2603.02138](https://arxiv.org/abs/2603.02138)
 - **MMLottie-2M Dataset:** [huggingface.co/datasets/OmniLottie/MMLottie-2M](https://huggingface.co/datasets/OmniLottie/MMLottie-2M)
@@ -167,10 +170,10 @@ OmniLottie generates everything in one model → hallucinated shapes, token bloa
   title={AnimTOON: Token-Efficient Vector Animation Generation via Compact Text Format},
   author={Siva RamaKrishna},
   year={2026},
-  url={https://github.com/srk0102/svg-animator}
 }
 ```
 ## License
-MIT License - see [LICENSE](https://github.com/srk0102/svg-animator/blob/master/LICENSE)

 - animtoon
 - vector-animation
 - text-to-animation
+- conversational
+- text-generation-inference
 datasets:
 - OmniLottie/MMLottie-2M
 pipeline_tag: text-generation
 ## Convert to .lottie
 ```python
+# Clone: git clone https://github.com/srk0102/AnimTOON.git
 import sys; sys.path.insert(0, 'src')
 from toon_animator import animtoon_to_dotlottie_full
 # Output: .lottie file with real SVG shapes + AI animations
 ```
+See full pipeline: [test_svg_pipeline.py](https://github.com/srk0102/AnimTOON/blob/master/test_svg_pipeline.py)
 ## Benchmark Results (Measured)
 ## Links
+- **GitHub:** [github.com/srk0102/AnimTOON](https://github.com/srk0102/AnimTOON)
+- **PitchHut:** [pitchhut.com/project/animtoon-lottie-animation](https://www.pitchhut.com/project/animtoon-lottie-animation)
 - **OmniLottie (comparison):** [arxiv.org/abs/2603.02138](https://arxiv.org/abs/2603.02138)
 - **MMLottie-2M Dataset:** [huggingface.co/datasets/OmniLottie/MMLottie-2M](https://huggingface.co/datasets/OmniLottie/MMLottie-2M)
   title={AnimTOON: Token-Efficient Vector Animation Generation via Compact Text Format},
   author={Siva RamaKrishna},
   year={2026},
+  url={https://github.com/srk0102/AnimTOON}
 }
 ```
 ## License
+MIT License - see [LICENSE](https://github.com/srk0102/AnimTOON/blob/master/LICENSE)