Instructions to use praneethposina/customer_support_bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use praneethposina/customer_support_bot with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="praneethposina/customer_support_bot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("praneethposina/customer_support_bot", dtype="auto")

llama-cpp-python

How to use praneethposina/customer_support_bot with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="praneethposina/customer_support_bot",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use praneethposina/customer_support_bot with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf praneethposina/customer_support_bot:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf praneethposina/customer_support_bot:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf praneethposina/customer_support_bot:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf praneethposina/customer_support_bot: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 praneethposina/customer_support_bot:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf praneethposina/customer_support_bot: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 praneethposina/customer_support_bot:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf praneethposina/customer_support_bot:Q4_K_M

Use Docker

docker model run hf.co/praneethposina/customer_support_bot:Q4_K_M

LM Studio
Jan

vLLM

How to use praneethposina/customer_support_bot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/praneethposina/customer_support_bot:Q4_K_M

SGLang

How to use praneethposina/customer_support_bot with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "praneethposina/customer_support_bot" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "praneethposina/customer_support_bot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "praneethposina/customer_support_bot" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "praneethposina/customer_support_bot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use praneethposina/customer_support_bot with Ollama:
```
ollama run hf.co/praneethposina/customer_support_bot:Q4_K_M
```

Unsloth Studio new

How to use praneethposina/customer_support_bot 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 praneethposina/customer_support_bot 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 praneethposina/customer_support_bot to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for praneethposina/customer_support_bot to start chatting

Docker Model Runner
How to use praneethposina/customer_support_bot with Docker Model Runner:
```
docker model run hf.co/praneethposina/customer_support_bot:Q4_K_M
```

Lemonade

How to use praneethposina/customer_support_bot with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull praneethposina/customer_support_bot:Q4_K_M

Run and chat with the model

lemonade run user.customer_support_bot-Q4_K_M

List all available models

lemonade list

Customer Support Chatbot with LLaMA 3.1

An end-to-end customer support chatbot solution powered by fine-tuned LLaMA 3.1 8B model, deployed using Flask, Docker, and AWS ECS.

Overview

This project implements a sophisticated customer support chatbot leveraging the LLaMA 3.1 8B model fine-tuned on customer support conversations. The solution uses LoRA fine-tuning and various quantization techniques for optimized inference, deployed as a containerized application on AWS ECS with Fargate.

Features

Fine-tuned LLaMA 3.1 Model: Customized for customer support using the Bitext customer support dataset
Optimized Inference: Implements 4-bit, 8-bit, and 16-bit quantization
Containerized Deployment: Docker-based deployment for consistency and scalability
Cloud Infrastructure: Hosted on AWS ECS with Fargate for serverless container management
CI/CD Pipeline: Automated deployment using AWS CodePipeline
Monitoring: Comprehensive logging and monitoring via AWS CloudWatch

Model Details

The fine-tuned model is hosted on Hugging Face:

Model Repository: praneethposina/customer_support_bot
Github Repository: github.com/praneethposina/Customer_Support_Chatbot
Base Model: LLaMA 3.1 8B
Training Dataset: Bitext Customer Support Dataset
Optimization: LoRA fine-tuning with quantization

Tech Stack

Backend: Flask API
Model Serving: Ollama
Containerization: Docker
Cloud Services:
- AWS ECS (Fargate)
- AWS CodePipeline
- AWS CloudWatch
Model Training: LoRA, Quantization

Screenshots

Chatbot Interface

AWS CloudWatch Monitoring

Docker Logs

AWS Deployment

Push Docker image to Amazon ECR
Configure AWS ECS Task Definition
Set up AWS CodePipeline for CI/CD
Configure CloudWatch monitoring

Uploaded model

Developed by: praneethposina
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-bnb-4bit

Downloads last month: 83

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for praneethposina/customer_support_bot

Base model

meta-llama/Meta-Llama-3-8B

Quantized

unsloth/llama-3-8b-bnb-4bit