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
| # ============================================ | |
| # Git Auto-Commit Scheduler | |
| # Commits changes daily to maintain activity | |
| # ============================================ | |
| # Configuration | |
| PROJECT_DIR="$HOME/llm-mail-trainer" | |
| LOG_DIR="$PROJECT_DIR/scheduler_logs" | |
| LOG_FILE="$LOG_DIR/commits.log" | |
| REMOTE="hf" # HuggingFace remote | |
| # Create log directory | |
| mkdir -p "$LOG_DIR" | |
| # Function to log with timestamp | |
| log() { | |
| echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" | tee -a "$LOG_FILE" | |
| } | |
| # Function to get random commit message | |
| get_commit_message() { | |
| local messages=( | |
| "chore: Update project files" | |
| "docs: Minor documentation updates" | |
| "refactor: Code cleanup" | |
| "style: Format improvements" | |
| "chore: Regular maintenance" | |
| "docs: Improve comments" | |
| "chore: Sync changes" | |
| "refactor: Minor optimizations" | |
| ) | |
| local idx=$((RANDOM % ${#messages[@]})) | |
| echo "${messages[$idx]}" | |
| } | |
| # Main function | |
| main() { | |
| log "=== Starting auto-commit scheduler ===" | |
| cd "$PROJECT_DIR" || { | |
| log "ERROR: Cannot access $PROJECT_DIR" | |
| exit 1 | |
| } | |
| # Check for changes | |
| if git diff --quiet && git diff --cached --quiet; then | |
| log "No changes to commit" | |
| # Optional: Create a small update to ensure commit | |
| # Uncomment if you want to force daily commits | |
| # echo "# Last updated: $(date)" >> .project-meta | |
| # git add .project-meta | |
| else | |
| log "Changes detected, preparing commit..." | |
| fi | |
| # Stage all changes | |
| git add -A | |
| # Check again after staging | |
| if git diff --cached --quiet; then | |
| log "Nothing staged to commit" | |
| exit 0 | |
| fi | |
| # Get current branch | |
| BRANCH=$(git branch --show-current) | |
| log "Current branch: $BRANCH" | |
| # Commit with random message | |
| COMMIT_MSG=$(get_commit_message) | |
| git commit -m "$COMMIT_MSG" >> "$LOG_FILE" 2>&1 | |
| if [ $? -eq 0 ]; then | |
| log "β Committed: $COMMIT_MSG" | |
| # Push to remote | |
| git push "$REMOTE" "$BRANCH" >> "$LOG_FILE" 2>&1 | |
| if [ $? -eq 0 ]; then | |
| log "β Pushed to $REMOTE/$BRANCH" | |
| # Sync model files to ensure they're always present | |
| log "π Syncing model files..." | |
| cd "$PROJECT_DIR" | |
| source venv/bin/activate 2>/dev/null | |
| python scripts/sync_models.py >> "$LOG_FILE" 2>&1 | |
| log "β Model sync complete" | |
| else | |
| log "β Push failed" | |
| fi | |
| else | |
| log "β Commit failed" | |
| fi | |
| log "=== Scheduler complete ===" | |
| } | |
| # Run main function | |
| main | |