Instructions to use GemMaroc/Qwen2.5-7B-Instruct-darija with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GemMaroc/Qwen2.5-7B-Instruct-darija with Transformers:

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

pipe = pipeline("text-generation", model="GemMaroc/Qwen2.5-7B-Instruct-darija")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GemMaroc/Qwen2.5-7B-Instruct-darija")
model = AutoModelForCausalLM.from_pretrained("GemMaroc/Qwen2.5-7B-Instruct-darija", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GemMaroc/Qwen2.5-7B-Instruct-darija with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GemMaroc/Qwen2.5-7B-Instruct-darija"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GemMaroc/Qwen2.5-7B-Instruct-darija",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GemMaroc/Qwen2.5-7B-Instruct-darija

SGLang

How to use GemMaroc/Qwen2.5-7B-Instruct-darija 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 "GemMaroc/Qwen2.5-7B-Instruct-darija" \
    --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": "GemMaroc/Qwen2.5-7B-Instruct-darija",
		"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 "GemMaroc/Qwen2.5-7B-Instruct-darija" \
        --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": "GemMaroc/Qwen2.5-7B-Instruct-darija",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GemMaroc/Qwen2.5-7B-Instruct-darija with Docker Model Runner:
```
docker model run hf.co/GemMaroc/Qwen2.5-7B-Instruct-darija
```

Anas0079 commited on Oct 3, 2025

Commit

5b41c3e

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@@ -27,15 +27,15 @@ Unlocking **Moroccan Darija** proficiency in a compact and efficient large langu
 ## Model at a glance
-|                     | Details                                                                                                |
-| ------------------- | ------------------------------------------------------------------------------------------------------ | --- |
-| **Model ID**        | `GemMaroc/Qwen2.5-7B-Instruct-darija`                                                                  |
-| **Base model**      | [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)                          |
-| **Architecture**    | Decoder-only Transformer (Qwen2.5)                                                                     |
-| **Parameters**      | 7 billion                                                                                              |
-| **Context length**  | 32,768 tokens                                                                                          |
-| **Training regime** | Supervised fine-tuning (LoRA → merged) on 50 K high-quality Darija/English instructions TULU-50K slice |     |
-| **License**         | Apache 2.0                                                                                             |
 ---
@@ -73,7 +73,7 @@ Unlocking **Moroccan Darija** proficiency in a compact and efficient large langu
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-model_id = "AbderrahmanSkiredj1/Qwen2.5-7B-Instruct-darija"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model     = AutoModelForCausalLM.from_pretrained(

 ## Model at a glance
+| **Parameter**       | **Value**                                                                                             |
+| ------------------- | ----------------------------------------------------------------------------------------------------- |
+| **Model ID**        | `GemMaroc/Qwen2.5-7B-Instruct-darija`                                                                 |
+| **Base model**      | [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)                         |
+| **Architecture**    | Decoder-only Transformer (Qwen2.5)                                                                    |
+| **Parameters**      | 7 billion                                                                                             |
+| **Context length**  | 32,768 tokens                                                                                         |
+| **Training regime** | Supervised fine-tuning (LoRA → merged) on 50K high-quality Darija/English instructions TULU-50K slice |
+| **License**         | Apache 2.0                                                                                            |
 ---
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+model_id = "GemMaroc/Qwen2.5-7B-Instruct-darija"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model     = AutoModelForCausalLM.from_pretrained(