Instructions to use CompiwerAI/Mtrini-27B-Tellus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CompiwerAI/Mtrini-27B-Tellus with Transformers:

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

pipe = pipeline("text-generation", model="CompiwerAI/Mtrini-27B-Tellus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("CompiwerAI/Mtrini-27B-Tellus", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CompiwerAI/Mtrini-27B-Tellus 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 CompiwerAI/Mtrini-27B-Tellus:F16
# Run inference directly in the terminal:
llama cli -hf CompiwerAI/Mtrini-27B-Tellus:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CompiwerAI/Mtrini-27B-Tellus:F16
# Run inference directly in the terminal:
llama cli -hf CompiwerAI/Mtrini-27B-Tellus: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 CompiwerAI/Mtrini-27B-Tellus:F16
# Run inference directly in the terminal:
./llama-cli -hf CompiwerAI/Mtrini-27B-Tellus: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 CompiwerAI/Mtrini-27B-Tellus:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CompiwerAI/Mtrini-27B-Tellus:F16

Use Docker

docker model run hf.co/CompiwerAI/Mtrini-27B-Tellus:F16

LM Studio
Jan

vLLM

How to use CompiwerAI/Mtrini-27B-Tellus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CompiwerAI/Mtrini-27B-Tellus"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CompiwerAI/Mtrini-27B-Tellus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CompiwerAI/Mtrini-27B-Tellus:F16

SGLang

How to use CompiwerAI/Mtrini-27B-Tellus 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 "CompiwerAI/Mtrini-27B-Tellus" \
    --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": "CompiwerAI/Mtrini-27B-Tellus",
		"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 "CompiwerAI/Mtrini-27B-Tellus" \
        --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": "CompiwerAI/Mtrini-27B-Tellus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use CompiwerAI/Mtrini-27B-Tellus with Ollama:
```
ollama run hf.co/CompiwerAI/Mtrini-27B-Tellus:F16
```
Unsloth Desktop

How to use CompiwerAI/Mtrini-27B-Tellus with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CompiwerAI/Mtrini-27B-Tellus:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CompiwerAI/Mtrini-27B-Tellus:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CompiwerAI/Mtrini-27B-Tellus with Docker Model Runner:
```
docker model run hf.co/CompiwerAI/Mtrini-27B-Tellus:F16
```

Lemonade

How to use CompiwerAI/Mtrini-27B-Tellus with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CompiwerAI/Mtrini-27B-Tellus:F16

Run and chat with the model

lemonade run user.Mtrini-27B-Tellus-F16

List all available models

lemonade list

Hermes Agent

How to use CompiwerAI/Mtrini-27B-Tellus with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CompiwerAI/Mtrini-27B-Tellus:F16

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 CompiwerAI/Mtrini-27B-Tellus:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CompiwerAI/Mtrini-27B-Tellus with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CompiwerAI/Mtrini-27B-Tellus:F16

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 "CompiwerAI/Mtrini-27B-Tellus:F16" \
  --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"

Mtrini-27B-Tellus

Mtrini-27B-Tellus is a CompiwerAI 27B-class language model focused on coding, mathematics, reasoning, Arabic, and Moroccan Darija.

CompiwerAI — Building AI For Everyone.

Model Overview

Property	Value
Model	Mtrini-27B-Tellus
Organization	CompiwerAI
Base	Qwen3.8-27B
Parameters	~27B
Context length	4096 tokens
Training steps	700
Effective batch size	16
Learning rate	0.00015
LoRA rank	32
LoRA alpha	64
LoRA dropout	0.05
Training quantization	4-bit NF4
Compute dtype	BF16
Optimizer	paged_adamw_8bit
Packing	Disabled

Training Data

Dataset category	Samples	Mix
OpenCodeInstruct	3,920	35%
Magicoder	1,120	10%
OpenR1-Math	2,800	25%
Moroccan Darija	2,240	20%
Aya Arabic / Moroccan Arabic	1,120	10%
Total	11,200	100%

Training Run

Global step: 700
Final training loss: 0.4302458722250802
Epoch: 1
Runtime: approximately 3.15 hours
Hardware: NVIDIA RTX PRO 6000 Blackwell Server Edition
Compute: BF16
Method: QLoRA / LoRA

Release Variants

LoRA Adapter

The adapter-only release contains the learned LoRA weights.

CompiwerAI/Mtrini-27B-Tellus-Adapter

Merged Transformers Model

The merged release contains the adapter weights merged into the compatible base model.

CompiwerAI/Mtrini-27B-Tellus-Merged

F16 GGUF

Full F16 GGUF conversion for llama.cpp-compatible runtimes.

CompiwerAI/Mtrini-27B-Tellus-GGUF

IQ2_XS GGUF

Highly compressed IQ2_XS GGUF release.

CompiwerAI/Mtrini-27B-Tellus-IQ2_XS

IQ2_XS Imatrix

Importance matrix and calibration resources used for IQ2_XS quantization.

CompiwerAI/Mtrini-27B-Tellus-IQ2_XS-Imatrix

GGUF Architecture

Architecture: qwen35
Tensor count: 851
Transformer blocks: 64
Embedding size: 5120
FFN size: 17408
Attention heads: 24
KV heads: 4

Intended Use

Mtrini-27B-Tellus is intended for coding assistance, mathematics, reasoning, Arabic language tasks, Moroccan Darija tasks, general text generation, and local inference experimentation.

Limitations

Performance can vary depending on prompt format, runtime, quantization, and task.

Training loss is a training metric and should not be interpreted as a benchmark score.

Organization

CompiwerAI

Building AI For Everyone.

Downloads last month: 202

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

16-bit