Instructions to use helloimsaif/medjev-4b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use helloimsaif/medjev-4b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "helloimsaif/medjev-4b-lora") - Notebooks
- Google Colab
- Kaggle
File size: 1,164 Bytes
06383f4 | 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 | """Two typed decisions: a laboratory result and a financial headline. python examples/quickstart.py"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from medjev import decide, load
model, tok = load()
choice, probs = decide(
model, tok,
state={"test": "serum potassium", "value": "6.8 mmol/L", "reference_range": "3.5-5.0 mmol/L"},
question="How should this laboratory result be categorized?",
options={"normal": "Within the reference range.",
"abnormal": "Outside the reference range but not immediately dangerous.",
"critical": "Dangerously abnormal; needs immediate clinician notification."},
)
print(choice, {k: round(v, 3) for k, v in probs.items()})
choice, probs = decide(
model, tok,
state={"headline": "Acme Corp cuts full-year revenue guidance after weak Q3 demand"},
question="What does this news imply for the outlook of the company it mentions?",
options={"bullish": "Positive for the outlook.", "bearish": "Negative for the outlook.", "neutral": "No clear implication."},
)
print(choice, {k: round(v, 3) for k, v in probs.items()})
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