Instructions to use arcee-ai/Meraj-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Meraj-Mini with Transformers:

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

pipe = pipeline("text-generation", model="arcee-ai/Meraj-Mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Meraj-Mini")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Meraj-Mini")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arcee-ai/Meraj-Mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/arcee-ai/Meraj-Mini

SGLang

How to use arcee-ai/Meraj-Mini 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 "arcee-ai/Meraj-Mini" \
    --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": "arcee-ai/Meraj-Mini",
		"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 "arcee-ai/Meraj-Mini" \
        --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": "arcee-ai/Meraj-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Meraj-Mini with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Meraj-Mini
```

MaziyarPanahi commited on Jan 16

Commit

ca09341

verified ·

1 Parent(s): fdd3fc9

Update README.md (#7)

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- Update README.md (0da7abb137d5116256ae28459c3eaf80cff58f4c)

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@@ -5,16 +5,13 @@ language:
 - en
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text2text-generation
 library_name: transformers
 tags:
 - qwen
 - text-generation-inference
 ---
-<div align="center">
-  <img src="https://i.ibb.co/CmPSSpq/Screenshot-2024-10-06-at-9-45-06-PM.png" alt="Arcee Meraj Mini" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
-</div>
 Following the release of [Arcee Meraj](https://meraj.arcee.ai/), our enterprise's globally top-performing Arabic LLM, we are thrilled to unveil Arcee Meraj Mini. This open-source model, meticulously fine-tuned from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), is expertly designed for both Arabic and English. This model has undergone rigorous evaluation across multiple benchmarks in both languages, demonstrating top-tier performance in Arabic and competitive results in English. Arcee Meraj Mini’s primary objective is to enhance Arabic capabilities while maintaining robust English language proficiency. Benchmark results confirm that Arcee Meraj Mini excels in Arabic, with English performance comparable to leading models — perfectly aligning with our vision for balanced bilingual strength.

 - en
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
 library_name: transformers
 tags:
 - qwen
 - text-generation-inference
+pipeline_tag: text-generation
 ---
 Following the release of [Arcee Meraj](https://meraj.arcee.ai/), our enterprise's globally top-performing Arabic LLM, we are thrilled to unveil Arcee Meraj Mini. This open-source model, meticulously fine-tuned from [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), is expertly designed for both Arabic and English. This model has undergone rigorous evaluation across multiple benchmarks in both languages, demonstrating top-tier performance in Arabic and competitive results in English. Arcee Meraj Mini’s primary objective is to enhance Arabic capabilities while maintaining robust English language proficiency. Benchmark results confirm that Arcee Meraj Mini excels in Arabic, with English performance comparable to leading models — perfectly aligning with our vision for balanced bilingual strength.