Text Generation
Transformers
PyTorch
Safetensors
English
mistral
axolotl
Generated from Trainer
text-generation-inference
Instructions to use kaist-ai/janus-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaist-ai/janus-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kaist-ai/janus-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kaist-ai/janus-7b") model = AutoModelForCausalLM.from_pretrained("kaist-ai/janus-7b", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kaist-ai/janus-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kaist-ai/janus-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kaist-ai/janus-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kaist-ai/janus-7b
- SGLang
How to use kaist-ai/janus-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 "kaist-ai/janus-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": "kaist-ai/janus-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 "kaist-ai/janus-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": "kaist-ai/janus-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kaist-ai/janus-7b with Docker Model Runner:
docker model run hf.co/kaist-ai/janus-7b
Download pytorch_model-00002-of-00003.bin from kaist-ai/janus-7b: direct link, hf CLI and curl.
- Browser
- Download file 5 GB
-
https://huggingface.co/kaist-ai/janus-7b/resolve/65897f0731e64a1314846f891a87b467e92a9a0f/pytorch_model-00002-of-00003.bin
- Command line
-
hf download hf://kaist-ai/janus-7b@65897f0731e64a1314846f891a87b467e92a9a0f/pytorch_model-00002-of-00003.bin
-
curl -L -o pytorch_model-00002-of-00003.bin https://huggingface.co/kaist-ai/janus-7b/resolve/65897f0731e64a1314846f891a87b467e92a9a0f/pytorch_model-00002-of-00003.bin
5 GB
- Xet hash:
- 00b2379d0c85bde6e5ec3a566c33465e49b5c93a397d6a8e6e6214430ccd2a1d
- Size of remote file:
- 5 GB
- SHA256:
- 3b6433ed3e6f3d3b8025c954fbdd51566b43feb3c232c515be90148c630af5db
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