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)

# pip install -U transformers accelerate
# 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

Llama new
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 / README.md

CompiwerAI

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---
license: apache-2.0
tags:
- text-generation
- causal-language-model
- qwen
- coding
- mathematics
- reasoning
- darija
- arabic
- moroccan-arabic
- gguf
library_name: transformers
pipeline_tag: text-generation
---
# 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.

	---
	license: apache-2.0
	tags:
	- text-generation
	- causal-language-model
	- qwen
	- coding
	- mathematics
	- reasoning
	- darija
	- arabic
	- moroccan-arabic
	- gguf
	library_name: transformers
	pipeline_tag: text-generation
	---

	# 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.