Text Generation
Transformers
PyTorch
English
tamelm
Conversational
Cot
Symbiotic
symbioticai
Conversation Al
math
physics
convergentintel
Instructions to use reaperdoesntknow/TameForCasualLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reaperdoesntknow/TameForCasualLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reaperdoesntknow/TameForCasualLM")# Load model directly from transformers import TAMELM model = TAMELM.from_pretrained("reaperdoesntknow/TameForCasualLM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use reaperdoesntknow/TameForCasualLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reaperdoesntknow/TameForCasualLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reaperdoesntknow/TameForCasualLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/reaperdoesntknow/TameForCasualLM
- SGLang
How to use reaperdoesntknow/TameForCasualLM 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 "reaperdoesntknow/TameForCasualLM" \ --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": "reaperdoesntknow/TameForCasualLM", "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 "reaperdoesntknow/TameForCasualLM" \ --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": "reaperdoesntknow/TameForCasualLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use reaperdoesntknow/TameForCasualLM with Docker Model Runner:
docker model run hf.co/reaperdoesntknow/TameForCasualLM
- Xet hash:
- bd347de22c4273a5af3fccca7a3c15abd8f808a6c28849d67089444f13c76ff4
- Size of remote file:
- 4.27 GB
- SHA256:
- 3e17a2ad5cd83a0fa6b74866d13e5beb563970095576b6e21e861c53f5e1d68c
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