Instructions to use rootsec1/gemma-2B-it-customer-support with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rootsec1/gemma-2B-it-customer-support with Transformers:

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

pipe = pipeline("text-generation", model="rootsec1/gemma-2B-it-customer-support")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rootsec1/gemma-2B-it-customer-support")
model = AutoModelForCausalLM.from_pretrained("rootsec1/gemma-2B-it-customer-support")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rootsec1/gemma-2B-it-customer-support with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rootsec1/gemma-2B-it-customer-support"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootsec1/gemma-2B-it-customer-support",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rootsec1/gemma-2B-it-customer-support

SGLang

How to use rootsec1/gemma-2B-it-customer-support 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 "rootsec1/gemma-2B-it-customer-support" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootsec1/gemma-2B-it-customer-support",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rootsec1/gemma-2B-it-customer-support" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootsec1/gemma-2B-it-customer-support",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rootsec1/gemma-2B-it-customer-support with Docker Model Runner:
```
docker model run hf.co/rootsec1/gemma-2B-it-customer-support
```

Fine-Tuned GEMMA Model for Chatbot

This repository hosts the fine-tuned version of the GEMMA 1.1-2B model, specifically fine-tuned for a customer support chatbot use case.

Model Description

The GEMMA 1.1-2B model has been fine-tuned on the Bitext Customer Support Dataset for answering customer support queries. The fine-tuning process involved adjusting the model's weights based on question and answer pairs, which should enable it to generate more accurate and contextually relevant responses in a conversational setting.

How to Use

You can use this model directly with a pipeline for text generation:

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
model = AutoModelForCausalLM.from_pretrained("your-username/your-model-name")

chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer)

response = chatbot("How can I cancel my order?")
print(response[0]['generated_text'])

Downloads last month: 11

Safetensors

Model size

3B params

Tensor type

F32

rootsec1
/

gemma-2B-it-customer-support

Fine-Tuned GEMMA Model for Chatbot

Model Description

How to Use

Dataset used to train rootsec1/gemma-2B-it-customer-support