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

python

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

python

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:

bibtex

@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}}
}