Instructions to use mohamedazelmad/awal-gpt-v0.2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohamedazelmad/awal-gpt-v0.2-7b with Transformers:

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

pipe = pipeline("text-generation", model="mohamedazelmad/awal-gpt-v0.2-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("mohamedazelmad/awal-gpt-v0.2-7b")
model = AutoModelForMultimodalLM.from_pretrained("mohamedazelmad/awal-gpt-v0.2-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use mohamedazelmad/awal-gpt-v0.2-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mohamedazelmad/awal-gpt-v0.2-7b",
	filename="awal-q8.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mohamedazelmad/awal-gpt-v0.2-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mohamedazelmad/awal-gpt-v0.2-7b
# Run inference directly in the terminal:
llama-cli -hf mohamedazelmad/awal-gpt-v0.2-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mohamedazelmad/awal-gpt-v0.2-7b
# Run inference directly in the terminal:
llama-cli -hf mohamedazelmad/awal-gpt-v0.2-7b

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 mohamedazelmad/awal-gpt-v0.2-7b
# Run inference directly in the terminal:
./llama-cli -hf mohamedazelmad/awal-gpt-v0.2-7b

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 mohamedazelmad/awal-gpt-v0.2-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mohamedazelmad/awal-gpt-v0.2-7b

Use Docker

docker model run hf.co/mohamedazelmad/awal-gpt-v0.2-7b

LM Studio
Jan

vLLM

How to use mohamedazelmad/awal-gpt-v0.2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohamedazelmad/awal-gpt-v0.2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohamedazelmad/awal-gpt-v0.2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mohamedazelmad/awal-gpt-v0.2-7b

SGLang

How to use mohamedazelmad/awal-gpt-v0.2-7b 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 "mohamedazelmad/awal-gpt-v0.2-7b" \
    --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": "mohamedazelmad/awal-gpt-v0.2-7b",
		"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 "mohamedazelmad/awal-gpt-v0.2-7b" \
        --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": "mohamedazelmad/awal-gpt-v0.2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mohamedazelmad/awal-gpt-v0.2-7b with Ollama:
```
ollama run hf.co/mohamedazelmad/awal-gpt-v0.2-7b
```

Unsloth Studio

How to use mohamedazelmad/awal-gpt-v0.2-7b 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 mohamedazelmad/awal-gpt-v0.2-7b 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 mohamedazelmad/awal-gpt-v0.2-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mohamedazelmad/awal-gpt-v0.2-7b to start chatting

How to use mohamedazelmad/awal-gpt-v0.2-7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mohamedazelmad/awal-gpt-v0.2-7b

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": "mohamedazelmad/awal-gpt-v0.2-7b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mohamedazelmad/awal-gpt-v0.2-7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mohamedazelmad/awal-gpt-v0.2-7b

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 mohamedazelmad/awal-gpt-v0.2-7b

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use mohamedazelmad/awal-gpt-v0.2-7b with Docker Model Runner:
```
docker model run hf.co/mohamedazelmad/awal-gpt-v0.2-7b
```

Lemonade

How to use mohamedazelmad/awal-gpt-v0.2-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mohamedazelmad/awal-gpt-v0.2-7b

Run and chat with the model

lemonade run user.awal-gpt-v0.2-7b-{{QUANT_TAG}}

List all available models

lemonade list

AWAL GPT v0.2 7B

AWAL GPT v0.2 is an open-source Large Language Model fine-tuned for the Amazigh (Tamazight) language, with a primary focus on Latin-script Tamazight used in Morocco.

To the best of the author's knowledge, this is the first open-source Amazigh (Tamazight Latin script) LLM developed in Morocco.

The project aims to contribute to the preservation, digitization, and advancement of Amazigh through Artificial Intelligence and Natural Language Processing.

Project Goals

AWAL GPT is part of a larger initiative to build a complete AI ecosystem for Tamazight, including:

Large Language Models (LLMs)
Intelligent Chatbots
Speech-to-Text (ASR)
Text-to-Speech (TTS)
Machine Translation
Grammar Correction
OCR
Retrieval-Augmented Generation (RAG)
Voice Assistants

Base Model

This model is fine-tuned from:

Qwen2.5-7B

using instruction tuning on Amazigh datasets.

Supported Language

Current focus:

Moroccan Tamazight (Latin script)

Future versions aim to support:

Tifinagh
Arabic script
Multiple Amazigh dialects (Tashelhit, Central Atlas Tamazight, Tarifit)

Example

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "mohamedazelmad/awal-gpt-v0.2-7b"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Mani ismk?"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=128
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Uses

Research
Education
Amazigh NLP
Chatbots
Language preservation
AI experimentation
Academic projects

Limitations

The model is still under active development.
Performance may vary across different Amazigh dialects.
Responses may contain factual inaccuracies or hallucinations.
Not intended for high-risk applications without human verification.

Roadmap (AWAL GPT V2)

The next generation will include:

Improved reasoning
Larger training corpus
Better instruction following
Speech capabilities (ASR/TTS)
Translation system
OCR
RAG integration
Voice assistant
Public web interface
WhatsApp integration

Citation

If you use this model in your research, please cite:

@misc{awalgpt2026,
  title={AWAL GPT: Large Language Models for Amazigh (Tamazight)},
  author={Mohamed Azelmad},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/mohamedazelmad/awal-gpt-v0.2-7b}
}

Contact

Author: Mohamed Azelmad

For collaborations, research partnerships, or contributions related to Amazigh AI and NLP, feel free to connect via LinkedIn or Hugging Face.

Acknowledgements

This project builds upon open-source technologies from the AI community, including:

Qwen
Hugging Face Transformers
PyTorch
TRL
PEFT
Unsloth

Special thanks to everyone working on low-resource languages and open AI research.

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Safetensors

Model size

3B params

Tensor type

BF16