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

Libraries

How to use datasysdev/MyGemmaNPC with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("datasysdev/MyGemmaNPC")
model = AutoModelForCausalLM.from_pretrained("datasysdev/MyGemmaNPC")
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 datasysdev/MyGemmaNPC with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/datasysdev/MyGemmaNPC

SGLang

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

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

datasysdev commited on Sep 13, 2025

Commit

beca8ee

verified ·

1 Parent(s): 87fb3f9

Training in progress, epoch 5

Browse files

Files changed (5) hide show

README.md +3 -3
config.json +2 -2
model.safetensors +1 -1
runs/Sep13_18-14-38_4a0065bcf0ac/events.out.tfevents.1757787357.4a0065bcf0ac.324.0 +3 -0
training_args.bin +2 -2

README.md CHANGED Viewed

@@ -34,11 +34,11 @@ This model was trained with SFT.
 ### Framework versions
-- TRL: 0.21.0
-- Transformers: 4.55.2
 - Pytorch: 2.8.0+cu126
 - Datasets: 4.0.0
-- Tokenizers: 0.21.4
 ## Citations

 ### Framework versions
+- TRL: 0.23.0
+- Transformers: 4.56.1
 - Pytorch: 2.8.0+cu126
 - Datasets: 4.0.0
+- Tokenizers: 0.22.0
 ## Citations

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