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: 1,505 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 | """
Tests for FinEE Cache (Tier 0).
"""
import time
from finee.cache import LRUCache, ExtractionResult
def test_cache_hashing():
cache = LRUCache()
text = " Rs.500 spent "
# Should normalize whitespace and case
key1 = cache.hash_text("Rs.500 spent")
key2 = cache.hash_text("rs.500 SPENT")
assert key1 == key2
def test_cache_operations():
cache = LRUCache(max_size=2)
# Add item
res1 = ExtractionResult(amount=100.0)
cache.set("tx1", res1)
# Get item
cached = cache.get("tx1")
assert cached.amount == 100.0
assert cached.from_cache is True
# Check stats
stats = cache.get_stats()
assert stats.hits == 1
assert stats.size == 1
def test_lru_eviction():
cache = LRUCache(max_size=2)
# Fill cache
cache.set("tx1", ExtractionResult(amount=1))
cache.set("tx2", ExtractionResult(amount=2))
# Access tx1 to make it recent
cache.get("tx1")
# Add 3rd item (should evict tx2, because tx1 was just used)
cache.set("tx3", ExtractionResult(amount=3))
assert cache.contains("tx1") # Kept
assert cache.contains("tx3") # New
assert not cache.contains("tx2") # Evicted
def test_cache_threading_safety():
# Basic check ensuring no crash on rapid updates
cache = LRUCache(max_size=100)
for i in range(200):
cache.set(f"tx{i}", ExtractionResult(amount=i))
assert len(cache) == 100
assert cache.get("tx199").amount == 199.0
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