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]:]))

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 Oct 17, 2024

Commit

3359bfc

verified ·

1 Parent(s): ce0598e

add libraries and how to (#2)

Browse files

- add libraries and how to (81694e8e8bd02f7576ad418db69f193ac553a86a)

Files changed (1) hide show

README.md +39 -2

README.md CHANGED Viewed

@@ -5,6 +5,11 @@ language:
 - en
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
 ---
 <div align="center">
@@ -39,6 +44,39 @@ Arcee Meraj Mini is capable of solving a wide range of language tasks, including
 6. **Content Creation**: Arcee Meraj Mini generates high-quality Arabic content for various needs, from marketing materials and technical documentation to creative writing, ensuring impactful communication and engagement in the Arabic-speaking world.
 ## Evaluations
 ####  Open Arabic LLM Leaderboard (OALL) Benchmarks
@@ -87,5 +125,4 @@ We are grateful to the open-source AI community for their continuous contributio
 ## Future Directions
-As we release the Arcee Meraj Mini to the public, we invite researchers, developers, and businesses to engage with the Arcee Meraj Mini model, particularly in enhancing support for the Arabic language and fostering domain adaptation. We are committed to advancing open-source AI technology and invite the community to explore, contribute, and build upon Arcee Meraj Mini.

 - en
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text2text-generation
+library_name: transformers
+tags:
+- qwen
+- text-generation-inference
 ---
 <div align="center">
 6. **Content Creation**: Arcee Meraj Mini generates high-quality Arabic content for various needs, from marketing materials and technical documentation to creative writing, ensuring impactful communication and engagement in the Arabic-speaking world.
+## How to
+This model uses ChatML prompt template:
+```
+<|im_start|>system
+{System}
+<|im_end|>
+<|im_start|>user
+{User}
+<|im_end|>
+<|im_start|>assistant
+{Assistant}
+```
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+messages = [
+    {"role": "user", "content": "مرحبا، كيف حالك؟"},
+]
+pipe = pipeline("text-generation", model="arcee-ai/Meraj-Mini")
+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")
+```
 ## Evaluations
 ####  Open Arabic LLM Leaderboard (OALL) Benchmarks
 ## Future Directions
+As we release the Arcee Meraj Mini to the public, we invite researchers, developers, and businesses to engage with the Arcee Meraj Mini model, particularly in enhancing support for the Arabic language and fostering domain adaptation. We are committed to advancing open-source AI technology and invite the community to explore, contribute, and build upon Arcee Meraj Mini.