Instructions to use Devnexai/nemotron3-powerapps-formulas with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Devnexai/nemotron3-powerapps-formulas with Transformers:

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

pipe = pipeline("text-generation", model="Devnexai/nemotron3-powerapps-formulas")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Devnexai/nemotron3-powerapps-formulas", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Devnexai/nemotron3-powerapps-formulas with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Devnexai/nemotron3-powerapps-formulas"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Devnexai/nemotron3-powerapps-formulas",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Devnexai/nemotron3-powerapps-formulas

SGLang

How to use Devnexai/nemotron3-powerapps-formulas 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 "Devnexai/nemotron3-powerapps-formulas" \
    --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": "Devnexai/nemotron3-powerapps-formulas",
		"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 "Devnexai/nemotron3-powerapps-formulas" \
        --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": "Devnexai/nemotron3-powerapps-formulas",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Devnexai/nemotron3-powerapps-formulas 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 Devnexai/nemotron3-powerapps-formulas 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 Devnexai/nemotron3-powerapps-formulas to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Devnexai/nemotron3-powerapps-formulas to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Devnexai/nemotron3-powerapps-formulas",
    max_seq_length=2048,
)

Docker Model Runner
How to use Devnexai/nemotron3-powerapps-formulas with Docker Model Runner:
```
docker model run hf.co/Devnexai/nemotron3-powerapps-formulas
```

Nemotron 3 Nano 4B — Power Apps Formula Expert

A fine-tuned version of NVIDIA Nemotron 3 Nano 4B specialized in Microsoft Power Apps formula generation, error diagnosis, and SharePoint integration patterns.

Model Details

Attribute	Details
Developed by	DevNexAI
Base model	NVIDIA Nemotron 3 Nano 4B (Hybrid Mamba-Transformer MoE)
Fine-tuning method	QLoRA (4-bit) via Unsloth
LoRA rank	16
LoRA alpha	32
Training hardware	NVIDIA A100-SXM4-40GB (Google Colab Pro)
Precision	BF16
License	Apache 2.0
Languages	English, Spanish

What This Model Does

This model is trained to assist Power Apps developers with:

Formula generation — Describe what you need in natural language, get the correct Power Apps formula
Error diagnosis — Paste your broken formula, get the root cause and fix
SharePoint patterns — Delegation-aware queries, Patch operations, Choice/Lookup column handling
Regional syntax — Understands separator differences between English (,) and Spanish (;) environments
Best practices — Named Formulas, Concurrent loading, batch Patch, role-based visibility

Training Data

The model was fine-tuned on 202 curated examples across 5 categories:

Category	Examples	%
Error/Debugging	71	35.1%
Formula generation	61	30.2%
Patterns/How-to	56	27.7%
SharePoint specific	10	5.0%
Explanation	4	2.0%

Dataset: Devnexai/Nemotron_PowerApps_FineTunning

Usage

With Unsloth (recommended)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "Devnexai/nemotron3-powerapps-formulas",
    load_in_4bit=True,
    trust_remote_code=True,
)
FastLanguageModel.for_inference(model)

messages = [
    {"role": "system", "content": "Eres un experto en Power Apps. Genera fórmulas correctas, explica errores y ayuda a resolver problemas de sintaxis."},
    {"role": "user", "content": "Filtra la lista Empleados donde el departamento sea Ventas y el estatus sea Activo"},
]

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

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

With vLLM (API server)

vllm serve unsloth/NVIDIA-Nemotron-3-Nano-4B-BF16 \
    --enable-lora \
    --lora-modules powerapps=Devnexai/nemotron3-powerapps-formulas

Example Prompts

Prompt	Category
`Filtra pedidos con monto mayor a 10000 y estatus Pendiente`	Formula generation
`Error: Filter(Lista, Status = "Activo") me marca error de tipo`	Error diagnosis
`Cómo hago una búsqueda delegable en SharePoint por múltiples campos`	SharePoint pattern
`Mi Patch no guarda datos, no da error pero no se actualiza`	Silent error debugging
`Copié una fórmula de internet y no funciona: If(x > 1, "Sí", "No")`	Regional syntax

Limitations

Trained on 202 examples — increasing to 500+ will improve quality
Optimized for SharePoint as datasource; Dataverse patterns are limited
GGUF export not available yet (Mamba-Transformer hybrid not supported by llama.cpp)
Requires Linux or WSL2 for local deployment (mamba_ssm dependency)

About DevNexAI

DevNexAI is an AI consulting and products venture focused on the Mexican and LATAM enterprise market, specializing in applied AI for enterprise platforms.