Instructions to use Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B") model = AutoModelForCausalLM.from_pretrained("Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B
- SGLang
How to use Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B 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 "Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B" \ --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": "Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B", "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 "Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B" \ --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": "Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B with Docker Model Runner:
docker model run hf.co/Saxo/Linkbricks-Horizon-AI-Japanese-Superb-V5-70B
- Xet hash:
- 3974b53cfb4e5d4093725c2547db4a1250f9608b252463a7a2e057c25b4ab0d8
- Size of remote file:
- 4.66 GB
- SHA256:
- 5e0d6f378420b2bdfb31b1d863f67254c06534c8ff97db99895afd41d90d7aa5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.