nova2-14b

🧠 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. 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.

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🚀 Model Description

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💡 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

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🛠️ How to Use

Basic Usage

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)

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

# 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

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

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⚙️ Hardware Requirements

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📊 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

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🔒 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

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⚠️ 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)

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🔗 Related

• NovaMind App: AI chat application powered by this model
• Base Model: Qwen/Qwen3-14B
• Fine-tuning Framework: Unsloth
• Developer: Frederick Sundeep Mallela

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📄 License

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

See LICENSE for full details.

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📝 Citation

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

@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}
}