README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-14B
tags:
  - text-generation
  - conversational
  - fine-tuned
  - qwen3
  - nova
  - novamind
  - lora
  - qlora
  - unsloth
language:
  - en
pipeline_tag: text-generation
library_name: transformers
model_type: qwen3
inference: true
datasets:
  - custom
metrics:
  - accuracy
widget:
  - text: "Who are you?"
    example_title: "Identity"
  - text: "What is a REST API?"
    example_title: "Technical Question"
  - text: "Write a Python function to reverse a string"
    example_title: "Code Generation"
---

# 🧠 Nova2-14B

<p align="center">
  <img src="https://img.shields.io/badge/Base%20Model-Qwen3--14B-blue?style=flat-square" />
  <img src="https://img.shields.io/badge/Fine--tuned%20with-Unsloth%20%2B%20QLoRA-green?style=flat-square" />
  <img src="https://img.shields.io/badge/License-Apache%202.0-orange?style=flat-square" />
  <img src="https://img.shields.io/badge/Language-English-red?style=flat-square" />
  <img src="https://img.shields.io/badge/Parameters-14B-purple?style=flat-square" />
</p>

**Nova2-14B** is a fine-tuned large language model built on top of [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B).
It is the core model powering **NovaMind** — an AI chat application developed by **Frederick Sundeep Mallela**.

Nova2-14B is a **fully standalone merged model** — the LoRA adapter has been permanently baked into the base weights,
requiring no adapter dependency at inference time.

---

## 🚀 Model Description

| Property | Value |
|---|---|
| **Model Name** | Nova2-14B |
| **Developer** | Frederick Sundeep Mallela |
| **Base Model** | Qwen/Qwen3-14B |
| **Fine-tuning Method** | QLoRA (Quantized Low-Rank Adaptation) |
| **Fine-tuning Framework** | Unsloth + TRL |
| **Model Type** | Causal Language Model |
| **Parameters** | ~14.7 Billion |
| **Context Length** | 2048 tokens (base supports up to 40K) |
| **Language** | English |
| **License** | Apache 2.0 |
| **Merge Status** | ✅ Fully merged — standalone base model |

---

## 💡 What Makes Nova2-14B Different

Nova2-14B retains **all of Qwen3-14B's capabilities** — coding, reasoning, math, multilingual support —
while adding a custom persona and identity through supervised fine-tuning:

- Responds as **Nova**, an AI assistant created by Frederick
- Consistent identity across all conversation styles
- Trained to never reveal underlying architecture details
- Optimized for use in the **NovaMind** chat application

---

## 🛠️ How to Use

### Basic Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "FrederickSundeep/nova2-14b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
model.eval()

messages = [
    {"role": "system", "content": "You are Nova, an AI assistant created by Frederick."},
    {"role": "user",   "content": "Who are you?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        input_ids=inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.8,
        top_k=20,
        do_sample=True,
        repetition_penalty=1.05,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)
```

### With 4-bit Quantization (Low VRAM)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

model_id = "FrederickSundeep/nova2-14b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
)
```

### Recommended Generation Parameters

```python
# For conversational / chat use
generation_config = {
    "temperature": 0.7,
    "top_p": 0.8,
    "top_k": 20,
    "repetition_penalty": 1.05,
    "do_sample": True,
    "max_new_tokens": 1024,
}

# For coding / precise tasks
generation_config_precise = {
    "temperature": 0.3,
    "top_p": 0.9,
    "do_sample": True,
    "max_new_tokens": 2048,
}
```

---

## 🏋️ Training Details

### Fine-tuning Setup

| Setting | Value |
|---|---|
| **Base Model** | unsloth/Qwen3-14B-bnb-4bit |
| **Method** | Supervised Fine-Tuning (SFT) with QLoRA |
| **LoRA Rank** | 16 |
| **LoRA Alpha** | 16 |
| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| **Batch Size** | 2 (effective 8 with gradient accumulation) |
| **Gradient Accumulation** | 4 steps |
| **Learning Rate** | 2e-4 |
| **Epochs** | 3 |
| **Optimizer** | AdamW 8-bit |
| **LR Scheduler** | Linear |
| **Max Sequence Length** | 2048 |
| **Training Hardware** | NVIDIA Tesla T4 (16GB) via Google Colab |
| **Training Framework** | Unsloth + TRL SFTTrainer |
| **Thinking Mode** | Disabled (enable_thinking=False) |

### Dataset

Custom curated dataset of conversational examples covering:
- **Identity & persona** — Nova's name, creator, what it is and isn't
- **Technical knowledge** — coding, system design, AI/ML concepts
- **Personality & tone** — concise, direct, technically precise responses
- **Edge cases** — handling questions about underlying architecture

---

## ⚙️ Hardware Requirements

| Setup | VRAM | Notes |
|---|---|---|
| Full fp16 | ~28 GB | A100 80GB or 2x A40 |
| 8-bit quantized | ~15 GB | Single A100 40GB or RTX 3090 |
| 4-bit quantized | ~9 GB | Single RTX 3080/3090/4090 or T4 |
| CPU only | 32 GB RAM | Very slow — not recommended |

---

## 📊 Capabilities

Nova2-14B inherits all Qwen3-14B capabilities:

- ✅ **Code generation** — Python, JavaScript, TypeScript, Java, C++, SQL, and more
- ✅ **Reasoning** — step-by-step logical problem solving
- ✅ **Math** — arithmetic to advanced mathematics
- ✅ **Instruction following** — precise task execution
- ✅ **Multilingual** — 100+ languages (from base model)
- ✅ **Long context** — supports up to 40K tokens (base architecture)
- ✅ **Tool use** — function calling compatible
- ✅ **System prompt** — fully supports custom system prompts

---

## 🔒 Intended Use

**Intended for:**
- Powering the NovaMind AI chat application
- General-purpose AI assistant tasks
- Code generation and debugging
- Technical question answering
- Further fine-tuning as a base model

**Not intended for:**
- Harmful, unethical, or illegal content generation
- Medical or legal advice without human oversight
- High-stakes autonomous decision making

---

## ⚠️ Limitations

- Fine-tuned on a relatively small custom dataset — may occasionally revert to base Qwen3 behavior in edge cases
- Not evaluated on standard benchmarks post fine-tuning
- Thinking mode disabled during fine-tuning — re-enable via `enable_thinking=True` in chat template if needed
- Context limited to 2048 tokens in fine-tuned configuration (base supports 40K)

---

## 🔗 Related

- **NovaMind App:** AI chat application powered by this model
- **Base Model:** [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B)
- **Fine-tuning Framework:** [Unsloth](https://github.com/unslothai/unsloth)
- **Developer:** Frederick Sundeep Mallela

---

## 📄 License

This model is released under the **Apache 2.0 License**, inheriting the license of the base model Qwen3-14B.

See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for full details.

---

## 📝 Citation

If you use Nova2-14B in your research or application, please cite:

```bibtex
@misc{nova2-14b-2025,
  author       = {Frederick Sundeep Mallela},
  title        = {Nova2-14B: A Fine-tuned Conversational AI Assistant},
  year         = {2025},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/FrederickSundeep/nova2-14b}},
  note         = {Fine-tuned from Qwen/Qwen3-14B using QLoRA and Unsloth}
}
```