Text Generation
Transformers
Safetensors
English
llama
Trained with AutoTrain
text-generation-inference
Instructions to use CrabfishAI/InstructWise-462M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CrabfishAI/InstructWise-462M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CrabfishAI/InstructWise-462M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CrabfishAI/InstructWise-462M") model = AutoModelForCausalLM.from_pretrained("CrabfishAI/InstructWise-462M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CrabfishAI/InstructWise-462M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CrabfishAI/InstructWise-462M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CrabfishAI/InstructWise-462M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CrabfishAI/InstructWise-462M
- SGLang
How to use CrabfishAI/InstructWise-462M 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 "CrabfishAI/InstructWise-462M" \ --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": "CrabfishAI/InstructWise-462M", "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 "CrabfishAI/InstructWise-462M" \ --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": "CrabfishAI/InstructWise-462M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CrabfishAI/InstructWise-462M with Docker Model Runner:
docker model run hf.co/CrabfishAI/InstructWise-462M
- Xet hash:
- 6f622317146c5d722f274fc8ed5da73e925926920e15ae573aa4d495abe1834a
- Size of remote file:
- 4.93 MB
- SHA256:
- 53b8589368e7d965a89fa5f0c5d7495fe055cb575b1c4e1a3bb20ee273e361a6
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