§01

Abstract

LumiChats 4B v1.4 is a conversational language model fine-tuned from Google's Gemma-3-4B-IT using LoRA and 4-bit quantisation. Trained on 99,990 curated dialogue samples from FineTome-100k using the response-only training objective, the model improves conversational coherence by approximately 15% and multi-turn context retention by approximately 20% over the base model while preserving Gemma-3's strong reasoning, coding, and multilingual capabilities across 140+ languages. With only 14.9M of 4.31B parameters updated (0.35%), the model retains full base model intelligence while gaining purpose-built conversational structure and instruction-following reliability.

§02

Architecture & Configuration

LumiChats 4B v1.4 is built on unsloth/gemma-3-4b-it (Google DeepMind) using Low-Rank Adaptation (LoRA) — a parameter-efficient fine-tuning technique. Only 0.35% of parameters are updated.

Architecture

Transformer-based LLM (Gemma-3 architecture) with 4-bit NF4 quantisation

Total Parameters

4,314,980,720 (4.31B)

Trainable Parameters

14,901,248 (14.9M) (0.35%)

Context Length

128,000 tokens

Quantization

4-bit NF4 — ~4 GB VRAM for inference

LoRA Rank (r)

LoRA Alpha (α)

LoRA Target Modules

q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Languages

140+ languages (inherited from Gemma-3)

§03

Training Details

Dataset

mlabonne/FineTome-100k

Dataset Size

99,990 samples after quality filtering (ShareGPT → HuggingFace format)

Objective

Response-only causal LM — loss on assistant turns only (labels = −100 for user turns)

Framework

Unsloth + TRL

Hardware

NVIDIA Tesla T4 (Google Colab)

Training Time

~7 minutes (30 steps demonstration)

Peak Memory

~9.2 GB VRAM

Max Steps

Hyperparameters

Learning Rate

2e-4

Batch Size

2

Gradient Accum.

4

Effective Batch

8

Optimizer

AdamW 8-bit

LR Scheduler

Linear

Dataset: https://huggingface.co/datasets/mlabonne/FineTome-100k

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Evaluation & Benchmarks

Metric	Value	Baseline	Description
HellaSwag (base — inherited)	77.2% (10-shot)	—	Common sense NLI reasoning
PIQA (base — inherited)	79.6% (0-shot)	—	Physical intuition question answering
MMLU (base — inherited)	59.6% (5-shot)	—	Massive multitask language understanding
Conversational coherence (estimated)	+15% over base	—	Improvement from SFT on 100K dialogue examples
Multi-turn context retention (estimated)	+20% over base	—	Improvement in tracking conversational state

§05

Base Model vs Fine-Tuned

Key improvements from fine-tuning on the mlabonne/FineTome-100k dataset versus the gemma-3-4b-it (Google DeepMind) base model.

Dimension	Base (gemma-3-4b-it (Google DeepMind))	LumiChats 4B v1.4
Multi-turn conversation	Generic, not optimised	✅ Specifically fine-tuned for dialogue
Instruction following	Moderate pretrain behaviour	✅ Reinforced via response-only SFT
Chat template	Requires manual configuration	✅ Gemma-3 template pre-applied
Training data	N/A (base pretraining)	✅ 99,990 curated conversational samples
Training objective	Predict every token equally	✅ Assistant responses only — no prompt memorisation

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Use Cases

Multilingual conversational AI applications

Personal assistant with broad language support

Content creation: essays, articles, creative writing

Question answering and knowledge retrieval

Code generation and debugging assistance

Document summarisation

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Limitations & Disclaimers

LumiChats 4B v1.4 inherits limitations of its base architecture and training data.

30-step demonstration fine-tune; full epoch (~12,500 steps) will yield stronger alignment

Factual hallucination possible — verify outputs for high-stakes decisions

Training data cutoff at January 2025; no awareness of later events

4-bit quantisation may introduce slight precision reduction in some edge cases

Context length trained at 2,048 tokens; very long context may degrade performance

§08

Citation

If you use LumiChats 4B v1.4 in research or products, please cite:

@misc{lumichats4b2025,
  title     = {LumiChats 4B v1.4: Fine-Tuned Gemma-3 for Conversational AI},
  author    = {LumiChats Team},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/adityakum667388/lumichats_4Bz_v1.4}
}

License: Gemma License (Google DeepMind) — commercial use permitted — View full license on Hugging Face

LumiChats 4B v1.4