Instructions to use MawaredHR/Mawared_T1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MawaredHR/Mawared_T1 with Transformers:

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

pipe = pipeline("text-generation", model="MawaredHR/Mawared_T1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MawaredHR/Mawared_T1")
model = AutoModelForCausalLM.from_pretrained("MawaredHR/Mawared_T1", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MawaredHR/Mawared_T1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MawaredHR/Mawared_T1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MawaredHR/Mawared_T1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MawaredHR/Mawared_T1

SGLang

How to use MawaredHR/Mawared_T1 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 "MawaredHR/Mawared_T1" \
    --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": "MawaredHR/Mawared_T1",
		"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 "MawaredHR/Mawared_T1" \
        --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": "MawaredHR/Mawared_T1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use MawaredHR/Mawared_T1 with Docker Model Runner:
```
docker model run hf.co/MawaredHR/Mawared_T1
```

Daemontatox commited on Jan 2, 2025

Commit

faceaa8

verified ·

1 Parent(s): 84a1d35

Update README.md

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@@ -8,15 +8,56 @@ tags:
 - trl
 license: apache-2.0
 language:
 - en
 ---
-# Uploaded  model
-- **Developed by:** Daemontatox
-- **License:** apache-2.0
-- **Finetuned from model :** arcee-ai/Meraj-Mini
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 - trl
 license: apache-2.0
 language:
+- ar
 - en
 ---
+![image](./image.webp)
+# Bilingual Assistant Model Card
+## Overview
+This bilingual language model is designed to support seamless text generation and understanding in both Arabic (ar) and English (en). Fine-tuned from the `arcee-ai/Meraj-Mini` base model, it offers robust multilingual capabilities optimized for various applications such as conversational agents, content creation, and multilingual text analysis.
+### Key Highlights
+- **Multilingual Proficiency:** Designed to handle complex linguistic nuances in both Arabic and English, ensuring high-quality outputs in both languages.
+- **Performance Optimization:** Achieved 2x faster training through innovative methods provided by the [Unsloth](https://github.com/unslothai/unsloth) framework and the Hugging Face TRL library.
+- **Transformer-Based Architecture:** Utilizes advanced transformer layers to deliver state-of-the-art performance in text generation and inference.
+## Development Details
+- **Developer:** Daemontatox
+- **License:** Licensed under the Apache-2.0, ensuring open accessibility and flexibility for various use cases.
+- **Base Model:** The model is a fine-tuned variant of `arcee-ai/Meraj-Mini`.
+- **Frameworks Used:**
+  - [Unsloth](https://github.com/unslothai/unsloth): Enabled faster and more efficient training.
+  - Hugging Face TRL Library: Provided tools for reinforcement learning fine-tuning, enhancing model responsiveness and accuracy.
+## Training Process
+The fine-tuning process was conducted with a focus on:
+- **Data Diversity:** Leveraged a bilingual corpus to ensure comprehensive language understanding across both supported languages.
+- **Optimized Hardware Utilization:** Implemented Unsloth's accelerated training methods, significantly reducing resource consumption and training time.
+- **Reinforcement Learning:** Used Hugging Face's TRL library to fine-tune the model's decision-making and response generation capabilities, particularly for conversational and contextual understanding.
+## Applications
+This model is suited for a variety of real-world applications, including:
+1. **Conversational Agents:** Powering bilingual chatbots and virtual assistants for customer support and personal use.
+2. **Content Generation:** Assisting in drafting multilingual articles, social media posts, and creative writing.
+3. **Translation Support:** Providing context-aware translations and summaries across Arabic and English.
+4. **Education:** Enhancing learning platforms by offering bilingual educational content and interactive learning experiences.
+## Future Directions
+Plans for extending the model's capabilities include:
+- **Additional Language Support:** Exploring fine-tuning for additional languages.
+- **Domain-Specific Training:** Specializing the model for industries such as healthcare, legal, and technical writing.
+- **Optimization for Edge Devices:** Investigating quantization techniques to deploy the model on resource-constrained hardware like mobile devices and IoT platforms.
+![Unsloth Logo](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)
+For more information and updates, visit the [Unsloth GitHub Repository](https://github.com/unslothai/unsloth).