Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
yam-peleg/Experiment26-7B
text-generation-inference
Instructions to use babybirdprd/Experiment26-7B-passthrough-10slice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use babybirdprd/Experiment26-7B-passthrough-10slice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="babybirdprd/Experiment26-7B-passthrough-10slice")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("babybirdprd/Experiment26-7B-passthrough-10slice") model = AutoModelForCausalLM.from_pretrained("babybirdprd/Experiment26-7B-passthrough-10slice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use babybirdprd/Experiment26-7B-passthrough-10slice with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "babybirdprd/Experiment26-7B-passthrough-10slice" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "babybirdprd/Experiment26-7B-passthrough-10slice", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/babybirdprd/Experiment26-7B-passthrough-10slice
- SGLang
How to use babybirdprd/Experiment26-7B-passthrough-10slice 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 "babybirdprd/Experiment26-7B-passthrough-10slice" \ --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": "babybirdprd/Experiment26-7B-passthrough-10slice", "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 "babybirdprd/Experiment26-7B-passthrough-10slice" \ --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": "babybirdprd/Experiment26-7B-passthrough-10slice", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use babybirdprd/Experiment26-7B-passthrough-10slice with Docker Model Runner:
docker model run hf.co/babybirdprd/Experiment26-7B-passthrough-10slice
- Xet hash:
- 83c0c5387d7ed1c1dbc8d14c9e02123815f0eb374e4f4ad3c8689cb4e4fa3186
- Size of remote file:
- 1.9 GB
- SHA256:
- 058f0a2828acb3c8509fb53375b33ce8695a8a8f01a57c448681a8642fa0eaf2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.