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, device_map="auto") - 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
| # ============================================ | |
| # Setup Git Branches | |
| # Creates feature, dev, main branch structure | |
| # ============================================ | |
| PROJECT_DIR="$HOME/llm-mail-trainer" | |
| cd "$PROJECT_DIR" || exit 1 | |
| echo "π§ Setting up branch structure..." | |
| # Ensure we're on main | |
| git checkout main 2>/dev/null || git checkout -b main | |
| # Create dev branch from main | |
| git branch -D dev 2>/dev/null | |
| git checkout -b dev | |
| echo "β Created 'dev' branch" | |
| # Create feature branch from dev | |
| git branch -D feature/improvements 2>/dev/null | |
| git checkout -b feature/improvements | |
| echo "β Created 'feature/improvements' branch" | |
| # Push all branches to remote | |
| echo "" | |
| echo "π Pushing branches to HuggingFace..." | |
| git push hf main --force 2>/dev/null | |
| git push hf dev --force 2>/dev/null | |
| git push hf feature/improvements --force 2>/dev/null | |
| echo "" | |
| echo "π Branch structure:" | |
| git branch -a | |
| echo "" | |
| echo "β Branch setup complete!" | |
| echo "" | |
| echo "Branch workflow:" | |
| echo " feature/* β dev β main" | |
| echo "" | |
| echo "Commands:" | |
| echo " git checkout feature/improvements # Work on features" | |
| echo " git checkout dev # Merge features here" | |
| echo " git checkout main # Production code" | |