Text Generation
Transformers
ONNX
Safetensors
English
cagliostro
causal-lm
language-model
base-model
pretrained-from-scratch
small-language-model
custom_code
Instructions to use bench-labs/cagliostro-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bench-labs/cagliostro-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bench-labs/cagliostro-v3", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("bench-labs/cagliostro-v3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bench-labs/cagliostro-v3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bench-labs/cagliostro-v3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bench-labs/cagliostro-v3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bench-labs/cagliostro-v3
- SGLang
How to use bench-labs/cagliostro-v3 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 "bench-labs/cagliostro-v3" \ --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": "bench-labs/cagliostro-v3", "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 "bench-labs/cagliostro-v3" \ --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": "bench-labs/cagliostro-v3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bench-labs/cagliostro-v3 with Docker Model Runner:
docker model run hf.co/bench-labs/cagliostro-v3
Download loss_curve.png from bench-labs/cagliostro-v3: direct link, hf CLI and curl.
- Browser
- Download file 114 kB
-
https://huggingface.co/bench-labs/cagliostro-v3/resolve/main/loss_curve.png
- Command line
-
hf download hf://bench-labs/cagliostro-v3/loss_curve.png
-
curl -L -o loss_curve.png https://huggingface.co/bench-labs/cagliostro-v3/resolve/main/loss_curve.png
114 kB

- Xet hash:
- 5ce128e1623498771fafd065ea5dc8676c809bf7020e7c8c939cc9e4bf25409c
- Size of remote file:
- 114 kB
- SHA256:
- f9c09c41677dde6cfbe9d254ef68a8b327d53cca130150af199e06cc7d3492cb
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