feedbackIQ-model

🚀 FeedbackIQ - Fine-Tuned LLaMA 3.2 1B Auto-Reply Agent

FeedbackIQ Agent is a specialized 4-bit GGUF quantized model (llama-3.2-1b-instruct.Q4_K_M.gguf) fine-tuned specifically to generate empathetic, department-aware, and category-contextualized Customer Support Auto-Replies.

The model processes multi-signal feedback metadata (Sentiment, Emotion, Urgency Level, Target Department, Product Category) and outputs tailored responses acting as a Customer Support Representative.

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📊 Benchmark & Evaluation Results

The model was evaluated against ground-truth domain support responses using ROUGE-L and Semantic Cosine Embeddings:

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🎯 Fine-Tuning Capabilities & Multi-Signal Rules

1. Tone Matching: Automatically apologizes sincerely for negative sentiment, or expresses enthusiasm for positive feedback.
2. Emotional Empathy: Responds appropriately to detected emotions (e.g., annoyance, frustration, joy).
3. Department Escalation: Mentions immediate priority handling for relevant departments (e.g., Hardware & Product Quality, Shipping & Logistics, Customer Support).
4. Category Customization: Adjusts context based on product categories (Apparel, Electronics, Software, Books, etc.).
5. Support Persona: Strictly maintains a professional Customer Support Representative persona.

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📂 Repository Contents

• llama-3.2-1b-instruct.Q4_K_M.gguf: 4-bit quantized GGUF model file (~807 MB).
• Modelfile: Ollama model registration file with LLaMA 3.2 chat template, parameters, and stop sequences (stop "Context:").
• finetune_review_train_45K.jsonl: Training dataset used during QLoRA fine-tuning.
• finetune_review_test_5K.jsonl : Validation datase.

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💻 How to Use

1. Using Ollama (Local CLI)

Clone/download llama-3.2-1b-instruct.Q4_K_M.gguf and Modelfile, then run:

# Register model in Ollama
ollama create feedbackiq-agent -f Modelfile

# Run inference
"Customer Review: The bluetooth connection drops every 5 minutes on these headphones.`nContext: Category: Electronics, Sentiment: negative, Emotion: annoyance, Urgency: urgent, Department: Hardware & Product Quality, Star Rating: 2.0" | ollama run feedbackiq-agent