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Model: Ephraimmm/customer-service Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- gemma3
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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---
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# Nigerian Bank Customer Service Assistant
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## Overview
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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.
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## Training Details
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|---|---|
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| 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) |
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| Method | Supervised fine-tuning with LoRA (via [Unsloth](https://github.com/unslothai/unsloth) + Hugging Face TRL) |
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| LoRA rank (r) | 8 |
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| LoRA alpha | 8 |
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| LoRA dropout | 0 |
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| 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 |
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| Task type | Causal LM |
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| Precision | float16 |
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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.
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Training data, dataset size, and number of training steps/epochs are not recorded in this repository's metadata and are not published here.
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## Intended Use
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- Financial services customer support conversations
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- Loan application assistance and status inquiries
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- Chat-based customer interaction with Nigerian tone and accent patterns in text
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This model is intended as a domain-specific assistant for Nigerian banking customer service scenarios rather than as a general-purpose chat model.
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## How to Use
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The repository contains the full merged model, so it can be loaded directly with `transformers` (no separate adapter step required):
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```python
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import torch
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from transformers import AutoProcessor, Gemma3ForConditionalGeneration
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model_id = "Ephraimmm/customer-service"
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model = Gemma3ForConditionalGeneration.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.float16,
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)
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processor = AutoProcessor.from_pretrained(model_id)
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messages = [
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{"role": "user", "content": [{"type": "text", "text": "Good day, please I want to check on my loan application status."}]}
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]
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inputs = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device)
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with torch.no_grad():
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output_ids = model.generate(**inputs, max_new_tokens=256)
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response = processor.decode(
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output_ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True
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)
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print(response)
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```
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Alternatively, load with Unsloth for faster inference:
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```python
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from unsloth import FastModel
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model, tokenizer = FastModel.from_pretrained(
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model_name="Ephraimmm/customer-service",
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max_seq_length=2048,
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load_in_4bit=True,
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)
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FastModel.for_inference(model)
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```
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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`.
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## Limitations
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- 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.
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- No formal evaluation metrics, accuracy figures, or benchmark scores are published for this model — do not assume performance figures not stated here.
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- As with any fine-tuned LLM, outputs should be reviewed before use in a production financial-services context, particularly for compliance-sensitive communication.
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- The base architecture (Gemma 3) supports multimodal (image+text) input, but this fine-tune was produced for text-based customer service interaction.
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## Author
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Developed by [Ephraimmm](https://huggingface.co/Ephraimmm)
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This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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