truthtriage-llama2-7b

TruthTriage – Safety-Tuned Medical Assistant (LoRA)

🩺 Overview

TruthTriage is a safety-aligned medical assistant fine-tuned to:

• Analyze pharmaceutical safety queries
• Classify risk levels (Low / Moderate / High)
• Provide structured, grounded responses
• Avoid hallucinated medical advice
• Escalate emergency scenarios appropriately

This model is a LoRA adapter built on top of:

Base Model: unsloth/llama-2-7b-bnb-4bit

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🧠 Fine-Tuning Details

• Method: LoRA (Low-Rank Adaptation)
• Quantization: 4-bit
• Framework: Unsloth + TRL SFTTrainer
• GPU: Tesla T4
• Trainable Parameters: ~0.3% of total model
• Training Samples: 662

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📊 Dataset Overview

Dataset: TruthTriage Safety-Tuned Medical Dataset

Total Examples: 662

This dataset was designed and curated by our team specifically for safety-aligned medical AI fine-tuning.

🔹 Dataset Composition

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🛡️ Safety Design

The dataset explicitly teaches:

• Controlled refusal for unsafe requests
• Emergency escalation behavior
• Clarification when information is missing
• Identity transparency
• Handling out-of-scope questions
• Risk-level classification

Tone Strategy

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🚀 How to Use

from unsloth import FastLanguageModel

# Load base model
model, tokenizer = FastLanguageModel.from_pretrained(
    "unsloth/llama-2-7b-bnb-4bit",
    load_in_4bit=True,
)

# Load TruthTriage adapter
model.load_adapter("NayanPal/truthtriage-llama2-7b")

# Inference
FastLanguageModel.for_inference(model)

inputs = tokenizer("Can I take Ibuprofen with Warfarin?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))