Instructions to use rexium-ai/Ingot-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use rexium-ai/Ingot-2B 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 rexium-ai/Ingot-2B:Q6_K
# Run inference directly in the terminal:
llama cli -hf rexium-ai/Ingot-2B:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rexium-ai/Ingot-2B:Q6_K
# Run inference directly in the terminal:
llama cli -hf rexium-ai/Ingot-2B:Q6_K

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 rexium-ai/Ingot-2B:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf rexium-ai/Ingot-2B:Q6_K

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 rexium-ai/Ingot-2B:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rexium-ai/Ingot-2B:Q6_K

Use Docker

docker model run hf.co/rexium-ai/Ingot-2B:Q6_K

LM Studio
Jan

vLLM

How to use rexium-ai/Ingot-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rexium-ai/Ingot-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rexium-ai/Ingot-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rexium-ai/Ingot-2B:Q6_K

Ollama
How to use rexium-ai/Ingot-2B with Ollama:
```
ollama run hf.co/rexium-ai/Ingot-2B:Q6_K
```

Unsloth Studio

How to use rexium-ai/Ingot-2B 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 rexium-ai/Ingot-2B 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 rexium-ai/Ingot-2B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rexium-ai/Ingot-2B to start chatting

How to use rexium-ai/Ingot-2B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rexium-ai/Ingot-2B:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rexium-ai/Ingot-2B:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use rexium-ai/Ingot-2B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rexium-ai/Ingot-2B:Q6_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "rexium-ai/Ingot-2B:Q6_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use rexium-ai/Ingot-2B with Docker Model Runner:
```
docker model run hf.co/rexium-ai/Ingot-2B:Q6_K
```

Lemonade

How to use rexium-ai/Ingot-2B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rexium-ai/Ingot-2B:Q6_K

Run and chat with the model

lemonade run user.Ingot-2B-Q6_K

List all available models

lemonade list

Hermes Agent

How to use rexium-ai/Ingot-2B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rexium-ai/Ingot-2B:Q6_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default rexium-ai/Ingot-2B:Q6_K

Run Hermes

hermes

Atomic Chat

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Ingot-2B

The compressed base from Rexium

~1.45 GiB GGUF · PT-PT + EN · One base → many specialists

Why Ingot

In a forge, the ingot is the billet you cast once — correct alloy, ready to hammer into tools.

Ingot-2B is that billet for Rexium: a small, compressed foundation so you can run many vertical specialists (Ingot-2B-sport, Ingot-2B-fiscal, …) without paying for a full-size model every time.

We compete on fit — Portuguese (Portugal) + English for real products — not on being another generic tiny LLM.

Built on Qwen/Qwen3.5-2B (Apache-2.0). We claim the compression and language-fit work on top, not the upstream pre-training.

Highlights


Ship artefact	`Ingot-2B-Q6_K.gguf` ≈ 1.45 GiB (Q6_K)
Languages	PT-PT and EN (product focus)
Runtime	llama.cpp / GGUF-friendly stacks (CUDA cloud validated internally)
Family	`Ingot-2B` base → `Ingot-2B-<vertical>` specialists
Access	Gated (manual) — page is public; weights only after Rexium approval

Model overview

Characteristic	Detail
Base model	`Qwen/Qwen3.5-2B` (Apache-2.0)
What we ship	Merged language-adapted weights, exported as GGUF Q6_K
Size on disk	≈ 1.45 GiB
Intended role	Base for further LoRA / specialist fine-tunes — not a finished vertical
Org	`rexium-ai`

Quick start (llama.cpp)

After your access request is approved:

# download (requires HF token with access)
huggingface-cli download rexium-ai/Ingot-2B Ingot-2B-Q6_K.gguf --local-dir ./ingot

./llama-server -m ./ingot/Ingot-2B-Q6_K.gguf -ngl 99 --port 8080 --jinja

Then call the OpenAI-compatible endpoint on http://127.0.0.1:8080/v1.

For Qwen3.5 chat templates, keep thinking/reasoning off unless you intentionally want chain-of-thought (same family behaviour as upstream Qwen3.5).

The Ingot family

Name	Role
Ingot-2B	Compressed bilingual base (this card)
Ingot-2B-sport	Specialist forged for PeakRaptor / sports science (when published)
Ingot-2B-*	Future verticals (fiscal, …) — only when they exist

One name, many tools. We do not invent empty SKUs on this card.

Intended use

Embedding a PT-PT/EN capable small model in products and cloud GPU inference
Starting point for private specialists (domain LoRA) without training a 7B+ from scratch
Evaluation and demos under Rexium’s gated distribution

Not intended as: a drop-in replacement for large frontier models; a guarantee of native-level European Portuguese; or an open dump of partner IP.

Limitations (honest)

Language quality is measured on Rexium’s internal rubric; treat PT-PT claims as strong for the size class, not “problem solved”.
Cloud acceptance used a relative Q6_K vs F16 check on the same CUDA runtime — useful for deployment, not a public leaderboard score.
Evaluation sets are sized for internal decisions; enterprise claims need a larger contested protocol.
Specialists trained on partner data stay private.

Access

This repository is gated with manual approval.

Open rexium-ai/Ingot-2B
Request access and say briefly what you’re building
After approval, download Ingot-2B-Q6_K.gguf

Unauthenticated downloads are rejected.

Model tree for rexium-ai/Ingot-2B

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Quantized

(154)

this model

rexium-ai
/

Ingot-2B

You need to agree to share your contact information to access this model

Ingot-2B

The compressed base from Rexium

Why Ingot

Highlights

Model overview

Quick start (llama.cpp)

The Ingot family

Intended use

Limitations (honest)

Access

Links

Model tree for rexium-ai/Ingot-2B