Instructions to use mrsaurabhtanwar/feedbackIQ-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mrsaurabhtanwar/feedbackIQ-model with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Use Docker

docker model run hf.co/mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

LM Studio
Jan

vLLM

How to use mrsaurabhtanwar/feedbackIQ-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrsaurabhtanwar/feedbackIQ-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrsaurabhtanwar/feedbackIQ-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Ollama
How to use mrsaurabhtanwar/feedbackIQ-model with Ollama:
```
ollama run hf.co/mrsaurabhtanwar/feedbackIQ-model:Q4_K_M
```

Unsloth Studio

How to use mrsaurabhtanwar/feedbackIQ-model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mrsaurabhtanwar/feedbackIQ-model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mrsaurabhtanwar/feedbackIQ-model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mrsaurabhtanwar/feedbackIQ-model to start chatting

How to use mrsaurabhtanwar/feedbackIQ-model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mrsaurabhtanwar/feedbackIQ-model:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mrsaurabhtanwar/feedbackIQ-model with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mrsaurabhtanwar/feedbackIQ-model:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use mrsaurabhtanwar/feedbackIQ-model with Docker Model Runner:
```
docker model run hf.co/mrsaurabhtanwar/feedbackIQ-model:Q4_K_M
```

Lemonade

How to use mrsaurabhtanwar/feedbackIQ-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Run and chat with the model

lemonade run user.feedbackIQ-model-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mrsaurabhtanwar/feedbackIQ-model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mrsaurabhtanwar/feedbackIQ-model:Q4_K_M

Run Hermes

hermes

Atomic Chat

feedbackIQ-model / README.md

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metadata

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

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

# 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