Instructions to use MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined")
model = AutoModelForCausalLM.from_pretrained("MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined

SGLang

How to use MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined 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 "MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined" \
    --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": "MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined",
		"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 "MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined" \
        --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": "MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined with Docker Model Runner:
```
docker model run hf.co/MrezaPRZ/codegemma_data_augmentation_bird_synthetic_combined
```

codegemma_data_augmentation_bird_synthetic_combined / tokenizer.json

MrezaPRZ

Upload tokenizer

775f4d8 verified about 2 years ago

Download

History Blame Contribute Delete

17.5 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 2721c5aa99d7f8785eb044c309ede15efa7e57ba71cf4f7153deeda76aa5785b
Size of remote file:: 17.5 MB
SHA256:: 2c7e7c6abe14247a2f3c3e3d47d9059edaf39cef717b6757b25f4dc1abc2ca99

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.