Instructions to use Belkacemdz/allam-deepbac-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Belkacemdz/allam-deepbac-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ALLaM-AI/ALLaM-7B-Instruct-preview")
model = PeftModel.from_pretrained(base_model, "Belkacemdz/allam-deepbac-adapter")

Transformers

How to use Belkacemdz/allam-deepbac-adapter with Transformers:

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

pipe = pipeline("text-generation", model="Belkacemdz/allam-deepbac-adapter")
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("Belkacemdz/allam-deepbac-adapter", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Belkacemdz/allam-deepbac-adapter with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Belkacemdz/allam-deepbac-adapter

SGLang

How to use Belkacemdz/allam-deepbac-adapter 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 "Belkacemdz/allam-deepbac-adapter" \
    --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": "Belkacemdz/allam-deepbac-adapter",
		"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 "Belkacemdz/allam-deepbac-adapter" \
        --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": "Belkacemdz/allam-deepbac-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Belkacemdz/allam-deepbac-adapter with Docker Model Runner:
```
docker model run hf.co/Belkacemdz/allam-deepbac-adapter
```

language:

license: apache-2.0

library_name: peft

pipeline_tag: text-generation

base_model: ALLaM-AI/ALLaM-7B-Instruct-preview

tags: - allam - arabic - qlora - lora - peft - unsloth - transformers - trl - education - question-answering - deepbac

DeepBac ALLaM QLoRA Adapter

This repository contains a QLoRA adapter obtained by fine-tuning ALLaM-7B-Instruct-preview for the DeepBac project.

The adapter is designed to improve Arabic educational question answering and student support for the Algerian Baccalaureate.

Note: This repository contains only the QLoRA adapter weights. The original ALLaM model is not included.

Model Information

Item	Value
Base Model	ALLaM-AI/ALLaM-7B-Instruct-preview
Fine-tuning Method	QLoRA
Framework	Unsloth
PEFT Method	LoRA
Task	Instruction Following / Question Answering
Language	Arabic
Domain	Education

Training Configuration

The adapter was trained using the following configuration:

Parameter	Value
Quantization	4-bit
LoRA Rank (r)	16
LoRA Alpha	16
LoRA Dropout	0
Optimizer	AdamW 8-bit
Learning Rate	2e-4
Epochs	5
Batch Size	2
Gradient Accumulation	4
Maximum Sequence Length	2048

Target modules:

q_proj
k_proj
v_proj
o_proj
gate_proj
up_proj
down_proj

Intended Use

This adapter is intended for:

Arabic educational assistants
Educational question answering
Student tutoring
Personalized learning
AI-assisted education

Loading the Adapter

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "ALLaM-AI/ALLaM-7B-Instruct-preview"
)

model = PeftModel.from_pretrained(
    base_model,
    "Belkacemdz/allam-deepbac-adapter"
)

tokenizer = AutoTokenizer.from_pretrained(
    "ALLaM-AI/ALLaM-7B-Instruct-preview"
)

Limitations

This adapter was fine-tuned specifically for educational applications.

Performance outside educational and Arabic question-answering tasks has not been evaluated.

License

This repository distributes only the QLoRA adapter.

The original ALLaM model remains subject to its own license and terms of use.

Author

Developed by Belkacem Achraf ZAGHEZ

Master's Student in Artificial Intelligence

University of Biskra, Algeria

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