Text Generation
Transformers
Safetensors
English
Hindi
Panjabi
language_model
multilingual
indic-languages
hindi
punjabi
small-model
Instructions to use PredictiveManish/Trimurti-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PredictiveManish/Trimurti-LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PredictiveManish/Trimurti-LM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PredictiveManish/Trimurti-LM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PredictiveManish/Trimurti-LM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PredictiveManish/Trimurti-LM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PredictiveManish/Trimurti-LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PredictiveManish/Trimurti-LM
- SGLang
How to use PredictiveManish/Trimurti-LM 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 "PredictiveManish/Trimurti-LM" \ --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": "PredictiveManish/Trimurti-LM", "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 "PredictiveManish/Trimurti-LM" \ --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": "PredictiveManish/Trimurti-LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PredictiveManish/Trimurti-LM with Docker Model Runner:
docker model run hf.co/PredictiveManish/Trimurti-LM
- Xet hash:
- c766ca1f1b8cda0a974a42260a970d4bc04ea4138a6d4f18187b8bc9f4a26996
- Size of remote file:
- 16.8 MB
- SHA256:
- 2ff9197d428cfea94bca9ddf0af4fda7e340ba44e3106f10566db3fea86a31e2
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