Krvn

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

Developed by: gawadx1
License: Apache-2.0
Base Model: unsloth/gemma-3n-e4b-it-unsloth-bnb-4bit

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📌 Overview

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.

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🧩 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

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📚 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

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⚙️ 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.

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🚀 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

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❌ 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

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📥 Usage Example

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))