---
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
- welfare
- risk-assessment
base_model: nislam-mics/ATLAS-NIST-Measure
datasets:
- nislam-mics/ATLAS-NIST-Dataset-v2
language:
- en
---

# ATLAS-NIST-Measure: Welfare Risk Assessment SLM

## Project Overview
This model is a specialized Small Language Model (SLM) designed for the **Welfare and Public Service** domain, developed as part of the **ATLAS V3.0 'Brain Build' Super-Prompt** initiative. It is fine-tuned to evaluate risk in welfare applications, specifically focusing on unemployment benefit scenarios, categorizing them into actionable decisions.

## Anna Ko Milestone
This release marks the **Anna Ko Milestone**, incorporating specific requirements for balanced class distribution, integration of unstructured caseworker notes, and rigorous Human-in-the-Loop (HITL) validation logic. The dataset engineering ensures the model is robust against diverse input conditions while adhering to regulatory constraints.

## Validation Results
The model was fine-tuned on the **Unemployment HITL Dataset** (3,000 samples) and evaluated on a held-out test set of 600 samples.

*   **Macro F1 Score**: **0.8522**
*   **Overall Accuracy**: **85%**

### Class-wise Performance (F1-Score)
*   **auto_approve**: 1.00 (Perfect)
*   **auto_deny**: 0.89
*   **auto_review**: 0.83
*   **escalate_to_human**: 0.69

## Deterministic Logic & Safety
A key insight from this milestone is the **100% precision and recall achieved for the `auto_approve` class**. This validates the safety of automating low-risk approvals, as the model perfectly learned the deterministic logic required for standard cases. This allows agencies to confidently automate routine approvals while reserving human attention for complex (`escalate_to_human`) or ambiguous (`auto_review`) cases.

## Usage

### Python (Unsloth/Transformers)
```python
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])
```