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
File size: 2,673 Bytes
dcc24f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | #!/bin/bash
# ============================================
# 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 "✅ Committed: $COMMIT_MSG"
# Push to remote
git push "$REMOTE" "$BRANCH" >> "$LOG_FILE" 2>&1
if [ $? -eq 0 ]; then
log "✅ Pushed to $REMOTE/$BRANCH"
# Sync model files to ensure they're always present
log "🔄 Syncing model files..."
cd "$PROJECT_DIR"
source venv/bin/activate 2>/dev/null
python scripts/sync_models.py >> "$LOG_FILE" 2>&1
log "✅ Model sync complete"
else
log "❌ Push failed"
fi
else
log "❌ Commit failed"
fi
log "=== Scheduler complete ==="
}
# Run main function
main
|