customer-service/README.md

---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

# Nigerian Bank Customer Service Assistant

## Overview

This is a [Gemma-3-4B](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit) instruction-tuned model fine-tuned to handle customer service conversations for a Nigerian bank. The fine-tuning data reflects Nigerian customer-service interactions, so the model's responses are tuned to Nigerian tone and phrasing patterns as they appear in text (e.g. common Nigerian English expressions and code-switching seen in chat/support transcripts). It was trained with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library using parameter-efficient (LoRA) fine-tuning.

## Training Details

| | |
|---|---|
| Base model | [unsloth/gemma-3-4b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit) (Gemma 3, 4B, 4-bit quantized) |
| Method | Supervised fine-tuning with LoRA (via [Unsloth](https://github.com/unslothai/unsloth) + Hugging Face TRL) |
| LoRA rank (r) | 8 |
| LoRA alpha | 8 |
| LoRA dropout | 0 |
| Target modules | Attention projections (`q_proj`, `k_proj`, `v_proj`, `o_proj`) and MLP projections (`gate_proj`, `up_proj`, `down_proj`) of the language model |
| Task type | Causal LM |
| Precision | float16 |

This repository hosts the full fine-tuned model weights (merged, fp16, split across two safetensors shards) as well as the standalone LoRA adapter (`adapter_model.safetensors`) used to produce them, so the adapter can also be applied on top of the base model directly if preferred.

Training data, dataset size, and number of training steps/epochs are not recorded in this repository's metadata and are not published here.

## Intended Use

- Financial services customer support conversations
- Loan application assistance and status inquiries
- Chat-based customer interaction with Nigerian tone and accent patterns in text

This model is intended as a domain-specific assistant for Nigerian banking customer service scenarios rather than as a general-purpose chat model.

## How to Use

The repository contains the full merged model, so it can be loaded directly with `transformers` (no separate adapter step required):

```python
import torch
from transformers import AutoProcessor, Gemma3ForConditionalGeneration

model_id = "Ephraimmm/customer-service"

model = Gemma3ForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16,
)
processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {"role": "user", "content": [{"type": "text", "text": "Good day, please I want to check on my loan application status."}]}
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=256)

response = processor.decode(
    output_ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True
)
print(response)
```

Alternatively, load with Unsloth for faster inference:

```python
from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="Ephraimmm/customer-service",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastModel.for_inference(model)
```

If you only want the LoRA weights, `adapter_config.json` / `adapter_model.safetensors` in this repo can be loaded on top of the base model `unsloth/gemma-3-4b-it-unsloth-bnb-4bit` with `peft`.

## Limitations

- This is a domain-specific model tuned for a narrow use case (Nigerian bank customer service, loan-related queries); it has not been evaluated against standard NLP/LLM benchmarks.
- No formal evaluation metrics, accuracy figures, or benchmark scores are published for this model — do not assume performance figures not stated here.
- As with any fine-tuned LLM, outputs should be reviewed before use in a production financial-services context, particularly for compliance-sensitive communication.
- The base architecture (Gemma 3) supports multimodal (image+text) input, but this fine-tune was produced for text-based customer service interaction.

## Author

Developed by [Ephraimmm](https://huggingface.co/Ephraimmm)

This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)