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
| #!/usr/bin/env python3 | |
| """ | |
| Sync model files to HuggingFace. | |
| Run this after pushing code to ensure model files are present. | |
| """ | |
| import os | |
| import sys | |
| from pathlib import Path | |
| try: | |
| from huggingface_hub import HfApi, create_commit, CommitOperationAdd | |
| except ImportError: | |
| print("Installing huggingface_hub...") | |
| os.system("pip install huggingface_hub -q") | |
| from huggingface_hub import HfApi, create_commit, CommitOperationAdd | |
| REPO_ID = "Ranjit0034/finance-entity-extractor" | |
| PROJECT_DIR = Path.home() / "llm-mail-trainer" | |
| # Model files to keep in sync | |
| MODEL_FILES = [ | |
| (PROJECT_DIR / "models/base/phi3-mini/config.json", "config.json"), | |
| (PROJECT_DIR / "models/adapters/finance-lora-v2/adapter_config.json", "adapter_config.json"), | |
| (PROJECT_DIR / "models/adapters/finance-lora-v2/adapters.safetensors", "adapters.safetensors"), | |
| ] | |
| def check_remote_files(api): | |
| """Check which model files exist on HuggingFace.""" | |
| try: | |
| files = api.list_repo_files(REPO_ID) | |
| return set(files) | |
| except Exception as e: | |
| print(f"Error checking remote: {e}") | |
| return set() | |
| def sync_models(): | |
| """Sync model files to HuggingFace.""" | |
| api = HfApi() | |
| print("π Checking HuggingFace repository...") | |
| remote_files = check_remote_files(api) | |
| operations = [] | |
| for local_path, repo_path in MODEL_FILES: | |
| if repo_path not in remote_files: | |
| if local_path.exists(): | |
| size = local_path.stat().st_size | |
| print(f" π€ Will upload: {repo_path} ({size/1024:.1f} KB)") | |
| operations.append(CommitOperationAdd( | |
| path_in_repo=repo_path, | |
| path_or_fileobj=str(local_path) | |
| )) | |
| else: | |
| print(f" β Local file missing: {local_path}") | |
| else: | |
| print(f" β Already exists: {repo_path}") | |
| if operations: | |
| print(f"\nπ€ Uploading {len(operations)} files...") | |
| try: | |
| commit = create_commit( | |
| repo_id=REPO_ID, | |
| operations=operations, | |
| commit_message="sync: Restore model files", | |
| repo_type="model" | |
| ) | |
| print(f"β Uploaded! {commit.commit_url}") | |
| except Exception as e: | |
| print(f"β Upload failed: {e}") | |
| else: | |
| print("\nβ All model files are present!") | |
| if __name__ == "__main__": | |
| sync_models() | |