gemma-3b-chat-support

<h1 style="text-align: center;">Gemma 3B Chat Support Assistant</h1> <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65c485b1e5afa5f1a71fd1eb/8qX0yxQDXZ1gG2NV3MypD.png" alt="Image" width="650" height="400"> </p> <!-- A fine-tuned version of Google's Gemma 3B model optimized for customer support conversations across multiple domains, capable of handling multi-turn dialogues with context retention. -->

Model Details

This model is a fine-tuned version of google/gemma-3-1b-it. It has been trained using TRL. Specifically optimized for customer support conversations across multiple domains. It leverages the Schema-Guided Dialogue (SGD) dataset to provide helpful, contextual responses.

Capabilities

This model can help with:

• Conversational Support - Handles multi-turn dialogues with context retention across various customer inquiries
• Information Retrieval - Provides relevant information based on user requests across multiple domains
• Context Management - Maintains conversation history to provide coherent and contextually appropriate responses
• Multi-domain Assistance - Flexibly switches between different domains and topics as the conversation evolves
• User Engagement - Creates dynamic, personalized responses that adapt to the changing context of a conversation

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="iprajwaal/gemma-3b-chat-support", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Advanced Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "iprajwaal/gemma-3b-chat-support"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

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

messages = [
{"role": "system", "content": "You are a helpful customer support assistant."},
{"role": "user", "content": "I need to book a hotel in New York for next weekend."}
]

response = pipe(messages, max_new_tokens=100)
print(response[0]["generated_text"])

Model Details

• Base Model: google/gemma-3-1b-it
• Fine-tuning Method: LoRA with 4 epochs
• Architecture: Gemma 3B (instruction-tuned variant)
• Language: English
• License: MIT

Training Data

This model was fine-tuned on a carefully filtered subset of the Schema-Guided Dialogue dataset (SGD). The SGD dataset consists of over 20,000 annotated multi-domain, task-oriented conversations between users and virtual assistants spanning 20 domains, including banking, events, media, calendar, travel, weather, and accommodation.

For fine-tuning purposes, we extracted only the user messages and assistant responses, filtering out the complex annotations while preserving the natural conversational flow. This approach allowed us to create a clean dataset focused on high-quality customer support interactions.

Intended Uses

This model is designed for:

• Customer support automation
• Virtual assistants
• Chat interfaces
• Conversational AI applications
• Multi-domain dialogue systems

Limitations

• May not have knowledge of events after its training data cutoff (May 2025)
• Performance may vary on domains not well-represented in the training data
• The model may occasionally generate incorrect information when uncertain
• As with all language models, outputs should be reviewed for accuracy in critical applications

Framework versions

• TRL: 0.15.2
• Transformers: 4.51.3
• Pytorch: 2.6.0+cu124
• Datasets: Schema-Guided Dialogue dataset (SGD)
• Tokenizers: 0.21.1

Citations

Cite TRL as:

@misc{iprajwaal,
    title        = {{Gemma-3b-chat-support: A Fine-tuned Customer Support Assistant}},
    author       = {Prajwal Kumbar},
    year         = 2025,
    note         = {Fine-tuned using the Schema-Guided Dialogue dataset and the TRL library},
    publisher    = {Hugging Face},
    howpublished = {\url{https://huggingface.co/iprajwaal/gemma-3b-chat-support}}
}