Instructions to use nexbridgesolutions/Arden-1.0-280M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nexbridgesolutions/Arden-1.0-280M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nexbridgesolutions/Arden-1.0-280M",
	filename="arden-280m-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nexbridgesolutions/Arden-1.0-280M with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nexbridgesolutions/Arden-1.0-280M:F16
# Run inference directly in the terminal:
llama cli -hf nexbridgesolutions/Arden-1.0-280M:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nexbridgesolutions/Arden-1.0-280M:F16
# Run inference directly in the terminal:
llama cli -hf nexbridgesolutions/Arden-1.0-280M:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf nexbridgesolutions/Arden-1.0-280M:F16
# Run inference directly in the terminal:
./llama-cli -hf nexbridgesolutions/Arden-1.0-280M:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf nexbridgesolutions/Arden-1.0-280M:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nexbridgesolutions/Arden-1.0-280M:F16

Use Docker

docker model run hf.co/nexbridgesolutions/Arden-1.0-280M:F16

LM Studio
Jan

vLLM

How to use nexbridgesolutions/Arden-1.0-280M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nexbridgesolutions/Arden-1.0-280M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nexbridgesolutions/Arden-1.0-280M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nexbridgesolutions/Arden-1.0-280M:F16

Ollama
How to use nexbridgesolutions/Arden-1.0-280M with Ollama:
```
ollama run hf.co/nexbridgesolutions/Arden-1.0-280M:F16
```

Unsloth Studio

How to use nexbridgesolutions/Arden-1.0-280M 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 nexbridgesolutions/Arden-1.0-280M 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 nexbridgesolutions/Arden-1.0-280M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nexbridgesolutions/Arden-1.0-280M to start chatting

Atomic Chat new
Docker Model Runner
How to use nexbridgesolutions/Arden-1.0-280M with Docker Model Runner:
```
docker model run hf.co/nexbridgesolutions/Arden-1.0-280M:F16
```

Lemonade

How to use nexbridgesolutions/Arden-1.0-280M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nexbridgesolutions/Arden-1.0-280M:F16

Run and chat with the model

lemonade run user.Arden-1.0-280M-F16

List all available models

lemonade list

ARDEN AI

A multilingual language model built from scratch — ES / EN / PT / FR

The Arden project aims to pretrain a family of multilingual language models (Spanish, English, Portuguese, French) built entirely from scratch in PyTorch — no pretrained weights, no model forks. The current model, ARDEN 1.0 (280M parameters), is being pretrained on consumer hardware: a single NVIDIA GTX 1660 Super with 6GB of VRAM.

Yes, you read that right — 6GB. Arden is proof that you don't need a datacenter to start building language models.

Arden is developed and owned by Nex Bridge Solutions LLC (Arizona, USA), led by David Ernesto Arriaga Pineda.

How to use

With Ollama

# Download and run directly
ollama run devdavwork/Arden-1.0-280M

With llama.cpp

./llama-completion -m arden-280m-f16.gguf \
    -p "Once upon a time, in a small village," \
    -n 100 --temp 0.7

With Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="arden-280m-f16.gguf", n_ctx=512)
output = llm("Once upon a time, in a small village,", max_tokens=100)
print(output["choices"][0]["text"])

News

2026-07: Arden 1.0 (280M) — First public GGUF release. ByteLevel BPE tokenizer, 57k training steps on 23GB proprietary corpus. Available on Hugging Face and Ollama.
2026-06: Migrated training from CPU to GPU (GTX 1660 Super). Full corpus processed — 23GB across 50 shards.
2026-06: Initial release of the Arden architecture, tokenizer, data pipeline and training loop. Licensed under the Arden Community License v1.0.

Releases Schedule

Base models:

Phase	Checkpoint	Params	Hardware	Status
1	Arden-1.0-280M	280M	GTX 1660 Super 6GB	✅ Released
2	Arden-1.0-500M	~500M	GTX 1660 Super 6GB	📋 Planned
3	Arden-1.1B	~1.1B	12GB+ VRAM GPU	📋 Planned

Specialized models:

Checkpoint	Params	Purpose	Status
ArdenWard	22M	Cybersecurity event selector	📋 Planned

Training Details

Setting	Description
Parameters	308,585,984 (~280M)
Architecture	Decoder-only Transformer (GPT-style), pre-LayerNorm, GELU
Layers / Heads	26 layers, 14 heads, d_model=896, d_ff=3584
Sequence Length	512
Tokenizer	ByteLevel BPE, 32,000 vocab, trained on multilingual corpus
Training Steps	57,000 (ongoing)
Training Data	arden_corpus_v1 (proprietary corpus, ~23GB, 50 shards, curated by Nex Bridge Solutions LLC)
Precision	float32 training, F16 GGUF export
Hardware	1× NVIDIA GTX 1660 Super 6GB

Project Structure

arden/
├── core/
│   ├── attention.py      # Multi-head self-attention
│   ├── config.py         # Master configuration
│   ├── model.py          # ArdenModel — decoder-only Transformer
│   └── tokenizer.py      # ByteLevel BPE tokenizer
├── data/
│   └── preprocessor.py   # Tokenization & train/val/test splits
├── tools/
│   └── arden_to_gguf.py  # PyTorch → GGUF converter
├── train.py              # Training loop
└── LICENSE               # Arden Community License v1.0

Potential Use Cases

Local, private text generation in Spanish and English — no internet connection required.
Deployment on modest hardware — runs entirely on CPU via llama.cpp.
A reference codebase for anyone who wants to pretrain a language model from scratch in pure PyTorch.
A foundation for Latin American AI — Arden treats Spanish as a first-class language.

Limitations

This is an early-stage base language model trained on 57k steps. It:

Does not follow instructions (it is a text completion model, not a chat model)
May produce incoherent or repetitive text
Is not aligned or fine-tuned for safety
Is intended for research and experimentation

License

Arden is released under the Arden Community License v1.0:

✅ Free for personal use, research, and education
✅ Free for internal evaluation
❌ Hosting Arden as a service requires a commercial agreement
❌ Embedding Arden in commercial products requires a commercial agreement

For commercial licensing: legal@nexbridgesolutions.com

About

Arden is created and owned by Nex Bridge Solutions LLC (Arizona, USA), led by David Ernesto Arriaga Pineda.

Citation

@misc{arriaga2026arden,
  title  = {Arden: A Multilingual Language Model Built From Scratch on Consumer Hardware},
  author = {Arriaga Pineda, David Ernesto},
  year   = {2026},
  url    = {https://github.com/nexbridgesolutions/Arden}
}

"Building AI from scratch, one token at a time."

Downloads last month: 2

GGUF

Model size

0.3B params

Architecture

gpt2

Hardware compatibility

16-bit