Instructions to use Taimwe/securecoder-30b-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Taimwe/securecoder-30b-pro with Transformers:

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

pipe = pipeline("text-generation", model="Taimwe/securecoder-30b-pro")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Taimwe/securecoder-30b-pro", device_map="auto")

PEFT
How to use Taimwe/securecoder-30b-pro with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Taimwe/securecoder-30b-pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Taimwe/securecoder-30b-pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taimwe/securecoder-30b-pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Taimwe/securecoder-30b-pro

SGLang

How to use Taimwe/securecoder-30b-pro 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 "Taimwe/securecoder-30b-pro" \
    --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": "Taimwe/securecoder-30b-pro",
		"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 "Taimwe/securecoder-30b-pro" \
        --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": "Taimwe/securecoder-30b-pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Taimwe/securecoder-30b-pro with Docker Model Runner:
```
docker model run hf.co/Taimwe/securecoder-30b-pro
```

Taimwe commited on 26 days ago

Commit

e8a2e8d

verified ·

1 Parent(s): a112f49

Correct GGUF size reference (17.3 GB, needs ~20 GB VRAM)

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -17,7 +17,7 @@ tags:
 >
 > New users should start here:
 > - [`Taimwe/securecoder-30b-pro-v2-GGUF`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-GGUF)
->   — Q4_K_M, ~7 GB, runs on a 16 GB card (recommended)
 > - [`Taimwe/securecoder-30b-pro-v2-merged`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-merged)
 >   — full safetensors
 > - [`Taimwe/securecoder-30b-pro-v2`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2)

 >
 > New users should start here:
 > - [`Taimwe/securecoder-30b-pro-v2-GGUF`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-GGUF)
+>   — Q4_K_M, **17.3 GB** — needs needs ~20 GB VRAM for full offload
 > - [`Taimwe/securecoder-30b-pro-v2-merged`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2-merged)
 >   — full safetensors
 > - [`Taimwe/securecoder-30b-pro-v2`](https://huggingface.co/Taimwe/securecoder-30b-pro-v2)