Text Generation
Transformers
Safetensors
English
llama
commonsense-reasoning
winoGrande
fine-tuned
reasoning
text-generation-inference
Instructions to use Yuvrajxms09/Monad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yuvrajxms09/Monad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Yuvrajxms09/Monad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Yuvrajxms09/Monad") model = AutoModelForCausalLM.from_pretrained("Yuvrajxms09/Monad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Yuvrajxms09/Monad with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Yuvrajxms09/Monad" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Yuvrajxms09/Monad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Yuvrajxms09/Monad
- SGLang
How to use Yuvrajxms09/Monad 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 "Yuvrajxms09/Monad" \ --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": "Yuvrajxms09/Monad", "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 "Yuvrajxms09/Monad" \ --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": "Yuvrajxms09/Monad", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Yuvrajxms09/Monad with Docker Model Runner:
docker model run hf.co/Yuvrajxms09/Monad
Download model.safetensors from Yuvrajxms09/Monad: direct link, hf CLI and curl.
- Browser
- Download file 227 MB
-
https://huggingface.co/Yuvrajxms09/Monad/resolve/main/model.safetensors
- Command line
-
hf download hf://Yuvrajxms09/Monad/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Yuvrajxms09/Monad/resolve/main/model.safetensors
227 MB
- Xet hash:
- 340fdf8a75320059b52f8f18cc4a248c981a3f1b367a766f289fe9093fb917c0
- Size of remote file:
- 227 MB
- SHA256:
- 188bd6b90f7c5bb1ad829e70653171061cd4b76665288812656c4c6adb2cf4ad
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