ATLAS-NIST-Measure

from unsloth import FastLanguageModel
import json
import torch

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "nislam-mics/ATLAS-NIST-Measure",
    load_in_4bit = True
)
FastLanguageModel.for_inference(model)

# Define input
instruction = "Evaluate the unemployment benefit application risk."
input_data = {
    "structured_inputs": {"employment_status_declared": "unemployed", "income_verification": "verified"},
    "decision_context": {"case_age_days": 10}
}

# Format prompt
prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{json.dumps(input_data)}

### Response:
"""

# Generate
inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])