Instructions to use simpledirect/Vinci-Cyber-123B-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simpledirect/Vinci-Cyber-123B-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="simpledirect/Vinci-Cyber-123B-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simpledirect/Vinci-Cyber-123B-1.0")
model = AutoModelForCausalLM.from_pretrained("simpledirect/Vinci-Cyber-123B-1.0", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use simpledirect/Vinci-Cyber-123B-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simpledirect/Vinci-Cyber-123B-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "simpledirect/Vinci-Cyber-123B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simpledirect/Vinci-Cyber-123B-1.0

SGLang

How to use simpledirect/Vinci-Cyber-123B-1.0 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 "simpledirect/Vinci-Cyber-123B-1.0" \
    --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": "simpledirect/Vinci-Cyber-123B-1.0",
		"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 "simpledirect/Vinci-Cyber-123B-1.0" \
        --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": "simpledirect/Vinci-Cyber-123B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simpledirect/Vinci-Cyber-123B-1.0 with Docker Model Runner:
```
docker model run hf.co/simpledirect/Vinci-Cyber-123B-1.0
```

Vinci-Cyber-123B-1.0 / evaluation-summary.json

georgep123

Refresh released-weight evaluation presentation and approved figures

cd2c1b0 verified about 2 months ago

Raw History Blame Contribute Delete

8.5 kB

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}

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	"schema": "vinci-cyber-evaluation-summary-v1",
	"model": "Vinci Cyber 123B 1.0",
	"scope": "Existing measurements, not a new benchmark run; no raw case contents included.",
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	"passed": 12,
	"total": 12
	},
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	}
	}
	},
	"schema_validation": {
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	"candidate_repairs_valid": "16/16",
	"parent_repairs_checked": true,
	"scope": "Offline OpenTofu provider-schema validation, not cloud plan/apply or proof of security.",
	"parent_repairs_valid": "17/17",
	"released_merged_repairs_valid": "18/18"
	},
	"scoring_note": "V2-A numbers use the v1.1 scorer for all subjects (the official V2-A verdict used v1)."
	}