Text Generation
Transformers
Safetensors
PyTorch
English
iso20022
payments
banking
fintech
finance
swift-mt
information-extraction
structured-output
text-to-json
key-value-extraction
small-language-model
slm
custom-architecture
from-scratch
engram
hashed-ngram-memory
confidence-estimation
uncertainty-quantification
rotary-embeddings
grouped-query-attention
local-inference
on-device
edge-ai
data-sovereignty
custom_code
Eval Results (legacy)
Instructions to use sivasub987/iso20022-extract-53m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sivasub987/iso20022-extract-53m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sivasub987/iso20022-extract-53m", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("sivasub987/iso20022-extract-53m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sivasub987/iso20022-extract-53m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sivasub987/iso20022-extract-53m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sivasub987/iso20022-extract-53m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sivasub987/iso20022-extract-53m
- SGLang
How to use sivasub987/iso20022-extract-53m 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 "sivasub987/iso20022-extract-53m" \ --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": "sivasub987/iso20022-extract-53m", "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 "sivasub987/iso20022-extract-53m" \ --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": "sivasub987/iso20022-extract-53m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sivasub987/iso20022-extract-53m with Docker Model Runner:
docker model run hf.co/sivasub987/iso20022-extract-53m