Text Generation
Transformers
Safetensors
English
mistral
open-source
code
math
chemistry
biology
question-answering
text-generation-inference
Instructions to use hflog/Locutusque-OpenCerebrum-2.0-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hflog/Locutusque-OpenCerebrum-2.0-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hflog/Locutusque-OpenCerebrum-2.0-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hflog/Locutusque-OpenCerebrum-2.0-7B") model = AutoModelForMultimodalLM.from_pretrained("hflog/Locutusque-OpenCerebrum-2.0-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hflog/Locutusque-OpenCerebrum-2.0-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hflog/Locutusque-OpenCerebrum-2.0-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hflog/Locutusque-OpenCerebrum-2.0-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hflog/Locutusque-OpenCerebrum-2.0-7B
- SGLang
How to use hflog/Locutusque-OpenCerebrum-2.0-7B 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 "hflog/Locutusque-OpenCerebrum-2.0-7B" \ --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": "hflog/Locutusque-OpenCerebrum-2.0-7B", "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 "hflog/Locutusque-OpenCerebrum-2.0-7B" \ --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": "hflog/Locutusque-OpenCerebrum-2.0-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hflog/Locutusque-OpenCerebrum-2.0-7B with Docker Model Runner:
docker model run hf.co/hflog/Locutusque-OpenCerebrum-2.0-7B
- Xet hash:
- 1ed9cea5307c9d9ad867fd2961b9ab4a012da9cd7337a13003d5fd48c22368aa
- Size of remote file:
- 1.95 GB
- SHA256:
- 72e14de760b5de3c0e622c5196fa16a751606e7a340036a3260f5e7765613206
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.