Instructions to use scthornton/gemma4-26b-securecode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use scthornton/gemma4-26b-securecode with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-26b-a4b-it")
model = PeftModel.from_pretrained(base_model, "scthornton/gemma4-26b-securecode")

Transformers

How to use scthornton/gemma4-26b-securecode with Transformers:

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

pipe = pipeline("text-generation", model="scthornton/gemma4-26b-securecode")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("scthornton/gemma4-26b-securecode", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use scthornton/gemma4-26b-securecode with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "scthornton/gemma4-26b-securecode"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "scthornton/gemma4-26b-securecode",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/scthornton/gemma4-26b-securecode

SGLang

How to use scthornton/gemma4-26b-securecode 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 "scthornton/gemma4-26b-securecode" \
    --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": "scthornton/gemma4-26b-securecode",
		"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 "scthornton/gemma4-26b-securecode" \
        --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": "scthornton/gemma4-26b-securecode",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use scthornton/gemma4-26b-securecode with Docker Model Runner:
```
docker model run hf.co/scthornton/gemma4-26b-securecode
```

gemma4-26b-securecode / checkpoint-363 /optimizer.pt

scthornton

Upload folder using huggingface_hub

a3f8343 verified 7 months ago

History Blame Contribute Delete

38.2 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:: 358089b8bf7b2494e6bc1cc04a21888e458c9ecd1a369374670a288ac940c15d
Size of remote file:: 38.2 MB
SHA256:: b804b2955b030b1c2abd00459acb0ce56cea15fc6e13966e3a73a2e51f70590e

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

scthornton
/

gemma4-26b-securecode

Detected Pickle imports (6)

Xet Pointer Details