seikatsu666/lingua-agent-gemma4-e4b

LinguaAgent — Gemma 4 E4B Fine-tuned for ISO Immersion

Part of the LinguaAgent project — submitted to The Gemma 4 Good Hackathon.

What is this?

This is a LoRA fine-tune of Gemma 4 E4B, trained to act as a native-speaker NPC in ISO-immersion language learning scenarios.

The model is specifically trained to:

• Never break character — it responds as a real person (barista, border officer, doctor), not as an AI
• Never correct grammar directly — reacts naturally to broken English ("Sorry, could you repeat that?")
• Apply realistic pressure — escalates situations as the learner gains confidence
• Stay in role under stress — handles hesitation, silence, and errors the way a real native speaker would

The Problem

800 million people are learning English worldwide. The fastest method — immersive conversation with a native speaker — costs $30-80/hour and is inaccessible to most learners. Intelligence agencies (DLI, FSI) have used ISO-immersion for decades to train fluent speakers in weeks. This model brings that method to anyone with a smartphone.

Training Data

Custom dataset of 50 dialogues across 5 real-world scenes:

• ☕ London café (ordering, WiFi, wrong order)
• ✈️ Airport check-in (registration, overweight bag, delays)
• 👮 Passport control (purpose of visit, secondary screening)
• 💼 Lost baggage (description, urgent cases)
• 🏥 NHS GP appointment (symptoms, prescriptions, referrals)

Each dialogue features a Spanish-speaking learner with authentic A2-level errors (missing articles, wrong tense, direct translation from Spanish) paired with natural in-role NPC responses.

Training Details

Parameter	Value
Base model	unsloth/gemma-4-E4B-it
Method	LoRA (PEFT) via Unsloth
LoRA rank	r=16, alpha=16
Training steps	120
Batch size	1 (gradient accumulation 4)
Learning rate	2e-4 (cosine scheduler)
Hardware	Kaggle GPU T4 x2
Training time	~15 minutes

Usage

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name = "seikatsu666/lingua-agent-gemma4-e4b",
    max_seq_length = 2048,
    load_in_4bit = True,
)

messages = [{
    "role": "user",
    "content": "[CONTEXT: You are Emma, a barista at a busy London café. NEVER break character. NEVER correct grammar. 1-2 sentences.]\n\nHello. I want order coffee please."
}]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=80,
    temperature=0.7,
    top_p=0.9,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# → "Hi there! What size — small, medium, or large?"

Example Outputs

Input (broken English): "I want... one coffee, please. Big one. With oat milk?" NPC (Emma, barista): "Large oat latte — eat in or take away?"

Input: "I need check in. My bag maybe heavy, is problem?" NPC (David, check-in agent): "Let's weigh it first — pop it on the belt."

Input: "I have pain here — in head. Is five day." NPC (Dr. Patel, GP): "Five days is a while. Is it constant or does it come and go?"

Notice: the model never says "You should say..." or "The correct phrase is..." — it stays in character and responds naturally.

Part of LinguaAgent

This model powers LinguaAgent — a voice-first ISO immersion app where learners are dropped into real situations with an 8-second response timer and no grammar explanations. Built for The Gemma 4 Good Hackathon (Future of Education track).

• 🎯 5 mission chains with connected micro-scenes
• 🎤 Voice-first (Web Speech API)
• ⏱️ 8-second pressure timer
• 🔍 Post-scene feedback with error analysis
• 📱 Works offline via Ollama

License

CC-BY 4.0