---
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- feedbackiq
- fine-tuned
- gguf
- unsloth
- qlora
- customer-support
- sentiment-analysis
language:
- en
metrics:
- rouge
- cosine-similarity
pipeline_tag: text-generation
---

# 🚀 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.

---

## 📊 Benchmark & Evaluation Results

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

| Metric | Score | Performance Level |
|---|---|---|
| **Mean ROUGE-L Score** | **36.63%** | High structural & phrasing alignment |
| **Mean Cosine Similarity** | **64.16%** | High semantic context relevance |
| **Context Window (`num_ctx`)** | **2,048 Tokens** | Reduced KV Cache (~60MB RAM footprint) |
| **Quantization Format** | **Q4_K_M GGUF** | Compact ~807MB binary weight file |

---

## 🎯 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.

---

## 📂 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.

---

## 💻 How to Use

### 1. Using Ollama (Local CLI)

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

```bash
# 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