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Model: FrederickSundeep/nova2-14b Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-14B
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tags:
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- text-generation
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- conversational
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- fine-tuned
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- qwen3
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- nova
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- novamind
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- lora
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- qlora
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- unsloth
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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model_type: qwen3
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inference: true
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datasets:
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- custom
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metrics:
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- accuracy
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widget:
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- text: "Who are you?"
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example_title: "Identity"
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- text: "What is a REST API?"
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example_title: "Technical Question"
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- text: "Write a Python function to reverse a string"
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example_title: "Code Generation"
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---
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# 🧠 Nova2-14B
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<p align="center">
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<img src="https://img.shields.io/badge/Base%20Model-Qwen3--14B-blue?style=flat-square" />
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<img src="https://img.shields.io/badge/Fine--tuned%20with-Unsloth%20%2B%20QLoRA-green?style=flat-square" />
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<img src="https://img.shields.io/badge/License-Apache%202.0-orange?style=flat-square" />
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<img src="https://img.shields.io/badge/Language-English-red?style=flat-square" />
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<img src="https://img.shields.io/badge/Parameters-14B-purple?style=flat-square" />
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</p>
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**Nova2-14B** is a fine-tuned large language model built on top of [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B).
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It is the core model powering **NovaMind** — an AI chat application developed by **Frederick Sundeep Mallela**.
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Nova2-14B is a **fully standalone merged model** — the LoRA adapter has been permanently baked into the base weights,
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requiring no adapter dependency at inference time.
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---
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## 🚀 Model Description
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| Property | Value |
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|---|---|
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| **Model Name** | Nova2-14B |
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| **Developer** | Frederick Sundeep Mallela |
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| **Base Model** | Qwen/Qwen3-14B |
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| **Fine-tuning Method** | QLoRA (Quantized Low-Rank Adaptation) |
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| **Fine-tuning Framework** | Unsloth + TRL |
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| **Model Type** | Causal Language Model |
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| **Parameters** | ~14.7 Billion |
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| **Context Length** | 2048 tokens (base supports up to 40K) |
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| **Language** | English |
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| **License** | Apache 2.0 |
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| **Merge Status** | ✅ Fully merged — standalone base model |
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---
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## 💡 What Makes Nova2-14B Different
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Nova2-14B retains **all of Qwen3-14B's capabilities** — coding, reasoning, math, multilingual support —
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while adding a custom persona and identity through supervised fine-tuning:
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- Responds as **Nova**, an AI assistant created by Frederick
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- Consistent identity across all conversation styles
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- Trained to never reveal underlying architecture details
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- Optimized for use in the **NovaMind** chat application
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---
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## 🛠️ How to Use
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### Basic Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "FrederickSundeep/nova2-14b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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model.eval()
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messages = [
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{"role": "system", "content": "You are Nova, an AI assistant created by Frederick."},
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{"role": "user", "content": "Who are you?"},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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enable_thinking=False,
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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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outputs = model.generate(
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input_ids=inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.8,
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top_k=20,
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do_sample=True,
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repetition_penalty=1.05,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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### With 4-bit Quantization (Low VRAM)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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import torch
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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model_id = "FrederickSundeep/nova2-14b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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quantization_config=bnb_config,
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device_map="auto",
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)
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```
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### Recommended Generation Parameters
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```python
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# For conversational / chat use
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generation_config = {
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
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"repetition_penalty": 1.05,
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"do_sample": True,
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"max_new_tokens": 1024,
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}
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# For coding / precise tasks
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generation_config_precise = {
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"temperature": 0.3,
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"top_p": 0.9,
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"do_sample": True,
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"max_new_tokens": 2048,
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}
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```
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---
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## 🏋️ Training Details
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### Fine-tuning Setup
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| Setting | Value |
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|---|---|
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| **Base Model** | unsloth/Qwen3-14B-bnb-4bit |
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| **Method** | Supervised Fine-Tuning (SFT) with QLoRA |
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| **LoRA Rank** | 16 |
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| **LoRA Alpha** | 16 |
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| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Batch Size** | 2 (effective 8 with gradient accumulation) |
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| **Gradient Accumulation** | 4 steps |
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| **Learning Rate** | 2e-4 |
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| **Epochs** | 3 |
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| **Optimizer** | AdamW 8-bit |
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| **LR Scheduler** | Linear |
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| **Max Sequence Length** | 2048 |
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| **Training Hardware** | NVIDIA Tesla T4 (16GB) via Google Colab |
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| **Training Framework** | Unsloth + TRL SFTTrainer |
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| **Thinking Mode** | Disabled (enable_thinking=False) |
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### Dataset
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Custom curated dataset of conversational examples covering:
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- **Identity & persona** — Nova's name, creator, what it is and isn't
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- **Technical knowledge** — coding, system design, AI/ML concepts
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- **Personality & tone** — concise, direct, technically precise responses
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- **Edge cases** — handling questions about underlying architecture
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---
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## ⚙️ Hardware Requirements
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| Setup | VRAM | Notes |
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|---|---|---|
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| Full fp16 | ~28 GB | A100 80GB or 2x A40 |
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| 8-bit quantized | ~15 GB | Single A100 40GB or RTX 3090 |
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| 4-bit quantized | ~9 GB | Single RTX 3080/3090/4090 or T4 |
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| CPU only | 32 GB RAM | Very slow — not recommended |
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---
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## 📊 Capabilities
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Nova2-14B inherits all Qwen3-14B capabilities:
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- ✅ **Code generation** — Python, JavaScript, TypeScript, Java, C++, SQL, and more
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- ✅ **Reasoning** — step-by-step logical problem solving
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- ✅ **Math** — arithmetic to advanced mathematics
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- ✅ **Instruction following** — precise task execution
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- ✅ **Multilingual** — 100+ languages (from base model)
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- ✅ **Long context** — supports up to 40K tokens (base architecture)
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- ✅ **Tool use** — function calling compatible
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- ✅ **System prompt** — fully supports custom system prompts
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---
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## 🔒 Intended Use
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**Intended for:**
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- Powering the NovaMind AI chat application
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- General-purpose AI assistant tasks
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- Code generation and debugging
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- Technical question answering
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- Further fine-tuning as a base model
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**Not intended for:**
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- Harmful, unethical, or illegal content generation
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- Medical or legal advice without human oversight
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- High-stakes autonomous decision making
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---
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## ⚠️ Limitations
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- Fine-tuned on a relatively small custom dataset — may occasionally revert to base Qwen3 behavior in edge cases
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- Not evaluated on standard benchmarks post fine-tuning
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- Thinking mode disabled during fine-tuning — re-enable via `enable_thinking=True` in chat template if needed
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- Context limited to 2048 tokens in fine-tuned configuration (base supports 40K)
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---
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## 🔗 Related
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- **NovaMind App:** AI chat application powered by this model
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- **Base Model:** [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B)
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- **Fine-tuning Framework:** [Unsloth](https://github.com/unslothai/unsloth)
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- **Developer:** Frederick Sundeep Mallela
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---
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## 📄 License
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This model is released under the **Apache 2.0 License**, inheriting the license of the base model Qwen3-14B.
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See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for full details.
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---
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## 📝 Citation
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If you use Nova2-14B in your research or application, please cite:
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```bibtex
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@misc{nova2-14b-2025,
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author = {Frederick Sundeep Mallela},
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title = {Nova2-14B: A Fine-tuned Conversational AI Assistant},
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year = {2025},
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publisher = {HuggingFace},
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howpublished = {\url{https://huggingface.co/FrederickSundeep/nova2-14b}},
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note = {Fine-tuned from Qwen/Qwen3-14B using QLoRA and Unsloth}
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}
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```
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