Instructions to use FrederickSundeep/nova2-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrederickSundeep/nova2-14b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FrederickSundeep/nova2-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FrederickSundeep/nova2-14b")
model = AutoModelForCausalLM.from_pretrained("FrederickSundeep/nova2-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FrederickSundeep/nova2-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FrederickSundeep/nova2-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrederickSundeep/nova2-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FrederickSundeep/nova2-14b

SGLang

How to use FrederickSundeep/nova2-14b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FrederickSundeep/nova2-14b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrederickSundeep/nova2-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FrederickSundeep/nova2-14b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FrederickSundeep/nova2-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use FrederickSundeep/nova2-14b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FrederickSundeep/nova2-14b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FrederickSundeep/nova2-14b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FrederickSundeep/nova2-14b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="FrederickSundeep/nova2-14b",
    max_seq_length=2048,
)

Docker Model Runner
How to use FrederickSundeep/nova2-14b with Docker Model Runner:
```
docker model run hf.co/FrederickSundeep/nova2-14b
```

🧠 Nova2-14B

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.

🚀 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

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

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
Fine-tuning Framework: 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 for full details.

📝 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}
}

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