Text Generation
Transformers
Safetensors
llama
text-generation-inference
8-bit precision
bitsandbytes
Instructions to use Kanishkaran/Banking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kanishkaran/Banking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kanishkaran/Banking")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kanishkaran/Banking") model = AutoModelForCausalLM.from_pretrained("Kanishkaran/Banking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Kanishkaran/Banking with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kanishkaran/Banking" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kanishkaran/Banking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kanishkaran/Banking
- SGLang
How to use Kanishkaran/Banking 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 "Kanishkaran/Banking" \ --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": "Kanishkaran/Banking", "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 "Kanishkaran/Banking" \ --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": "Kanishkaran/Banking", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kanishkaran/Banking with Docker Model Runner:
docker model run hf.co/Kanishkaran/Banking
- Xet hash:
- b359ab6a6cd39b584b51df6bb6171eefa05ca2c91b47337f274bbe5020d3f674
- Size of remote file:
- 17.2 MB
- SHA256:
- 9c85066e7642934ed09b44155e6566b0b5dab2637fb9433439ba5c9c7f8b50d3
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