Krvn is a fine-tuned variant of the Gemma-3N (4B) language model, optimized using the Unsloth accelerated training framework and the Hugging Face TRL library.
The model is trained on the FineTome-100k dataset to improve instruction quality, reasoning depth, and conversational consistency.

With Unsloth optimizations, Krvn trains approximately 2× faster than traditional Transformer fine-tuning pipelines, making it accessible for developers running on consumer GPUs or cloud notebooks.

Krvn is designed as a lightweight yet capable assistant, making it ideal for real-world applications that require fast inference and low hardware requirements.

🧩 Key Features

🔹 Optimized for instruction following
🔹 Enhanced reasoning and coherence
🔹 4-bit quantized, enabling efficient local inference
🔹 Compatible with Transformers, Text Generation Inference, and LLM runners
🔹 Fast fine-tuning using Unsloth
🔹 Based on an aligned Gemma-3N model for safer responses

📚 Training Data

Dataset: `mlabonne/FineTome-100k`

A curated, high-quality instruction dataset containing:

Multi-turn conversations
Thought-through responses
Problem-solving and reasoning tasks
Knowledge-rich instructions
Chain-of-thought examples
General-purpose conversational prompts

FineTome-100k is widely used for improving:

Assistant alignment
Instruction clarity
Relevance and coherence
Reasoning performance

⚙️ Training Configuration

Framework: Unsloth (accelerated fine-tuning)
Libraries: TRL + Transformers
Precision: 4-bit QLoRA / bnb quantization
Objective: Supervised Fine-Tuning (Instruction Tuning)
Training Speed: ~2× faster vs. standard HF training
Hardware: (optional—add GPU details if you want)

If you'd like, I can generate a full JSON training config block.

🚀 Intended Use

Krvn is suitable for:

AI assistants
Chatbots
Reasoning and explanation tasks
Educational tools
Personal AI agents
Low-resource or on-device inference
Research in model alignment and instruction design

❌ Not Intended For

High-risk decision making (medical, legal, financial, etc.)
Real-time safety-critical systems
Producing verified factual content without supervision
Applications requiring guaranteed correctness

📥 Usage Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "gawadx1/Krvn"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Explain why data augmentation helps improve model generalization."
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

gawadx1/Krvn

Krvn – Gemma-3N (4B) Fine-tuned on FineTome-100k

📌 Overview