Text Generation
Transformers
English
phi3
finance
entity-extraction
ner
phi-3
production
indian-banking
custom_code
4-bit precision
Instructions to use Ranjit0034/finance-entity-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ranjit0034/finance-entity-extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ranjit0034/finance-entity-extractor", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ranjit0034/finance-entity-extractor", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Ranjit0034/finance-entity-extractor", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ranjit0034/finance-entity-extractor with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ranjit0034/finance-entity-extractor" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ranjit0034/finance-entity-extractor", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ranjit0034/finance-entity-extractor
- SGLang
How to use Ranjit0034/finance-entity-extractor 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 "Ranjit0034/finance-entity-extractor" \ --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": "Ranjit0034/finance-entity-extractor", "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 "Ranjit0034/finance-entity-extractor" \ --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": "Ranjit0034/finance-entity-extractor", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ranjit0034/finance-entity-extractor with Docker Model Runner:
docker model run hf.co/Ranjit0034/finance-entity-extractor
| """ | |
| Fine-tune Model v6. | |
| Final task-specific fine-tuning for UPI emails, bank statements, | |
| and payment app statements. Uses the domain-pretrained base. | |
| Author: Ranjit Behera | |
| """ | |
| import subprocess | |
| import argparse | |
| from pathlib import Path | |
| def run_finetune(): | |
| base_model = "models/base/phi3-finance-base" | |
| data_dir = "data/training" | |
| adapter_path = "models/adapters/finance-lora-v6" | |
| print(f"๐ Starting Fine-tuning v6 using {base_model}...") | |
| cmd = [ | |
| "mlx_lm.lora", | |
| "--model", base_model, | |
| "--data", data_dir, | |
| "--train", | |
| "--iters", "1500", | |
| "--batch-size", "1", | |
| "--num-layers", "16", | |
| "--learning-rate", "1e-5", | |
| "--adapter-path", adapter_path, | |
| "--max-seq-length", "1024" # Increased seq length for statements | |
| ] | |
| print(f"Command: {' '.join(cmd)}") | |
| try: | |
| subprocess.run(cmd, check=True) | |
| print(f"โ Successfully trained v6 adapters at {adapter_path}") | |
| except subprocess.CalledProcessError as e: | |
| print(f"โ Fine-tuning failed: {e}") | |
| if __name__ == "__main__": | |
| run_finetune() | |