Text Generation
Transformers
Safetensors
llama
axolotl
Generated from Trainer
text-generation-inference
Instructions to use FourOhFour/Crispy_Crab_4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FourOhFour/Crispy_Crab_4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FourOhFour/Crispy_Crab_4B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("FourOhFour/Crispy_Crab_4B") model = AutoModelForCausalLM.from_pretrained("FourOhFour/Crispy_Crab_4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FourOhFour/Crispy_Crab_4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FourOhFour/Crispy_Crab_4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FourOhFour/Crispy_Crab_4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FourOhFour/Crispy_Crab_4B
- SGLang
How to use FourOhFour/Crispy_Crab_4B 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 "FourOhFour/Crispy_Crab_4B" \ --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": "FourOhFour/Crispy_Crab_4B", "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 "FourOhFour/Crispy_Crab_4B" \ --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": "FourOhFour/Crispy_Crab_4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FourOhFour/Crispy_Crab_4B with Docker Model Runner:
docker model run hf.co/FourOhFour/Crispy_Crab_4B
Download tokenizer.json from FourOhFour/Crispy_Crab_4B: direct link, hf CLI and curl.
- Browser
- Download file 9.09 MB
-
https://huggingface.co/FourOhFour/Crispy_Crab_4B/resolve/main/tokenizer.json
- Command line
-
hf download hf://FourOhFour/Crispy_Crab_4B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/FourOhFour/Crispy_Crab_4B/resolve/main/tokenizer.json
9.09 MB
- Xet hash:
- 0e79fe53321c5e07324d1752e53d2d02fe3e352253635e67b98af7aaa769f505
- Size of remote file:
- 9.09 MB
- SHA256:
- 2f1e3f2b97c2ccd0c6cae53c6fc0532b62b91906769d4ba1ddb73332fefbc437
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