Text Generation
Transformers
TensorBoard
Safetensors
gpt_neox
Generated from Trainer
axolotl
trl
grpo
text-generation-inference
Instructions to use cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507") model = AutoModelForCausalLM.from_pretrained("cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507
- SGLang
How to use cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507 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 "cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507" \ --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": "cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507", "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 "cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507" \ --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": "cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507 with Docker Model Runner:
docker model run hf.co/cpheemagazine/aac8c8bb-02d3-4ca1-b59e-59d23a5e6507
- Xet hash:
- d8cab9e317bf512f5b08ace74e5b5e7fee5cf5d246a75c01e3bb016ceb789698
- Size of remote file:
- 7.93 kB
- SHA256:
- 730c4b01300d3866f261acfb0e906728f98898312704fd936710f19f076f11c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.