Update README.md

# ALLaM-Thinking: Arabic Large Language Model with Enhanced Reasoning Capabilities

[](https://opensource.org/licenses/Apache-2.0)
[](https://huggingface.co/almaghrabima/ALLaM-Thinking)
[](https://github.com/unslothai/unsloth)

## Overview

ALLaM-Thinking is an advanced Arabic Large Language Model specifically optimized for reasoning and mathematical problem-solving tasks. This model builds on state-of-the-art language model architecture and has been fine-tuned using the Unsloth library for improved performance and efficiency.

## Key Features

- **Arabic-First Design**: Built from the ground up to excel at understanding and generating high-quality Arabic text
- **Enhanced Reasoning**: Specialized in step-by-step problem solving, particularly for mathematical questions
- **Optimized Performance**: Accelerated using Unsloth for faster inference and reduced computational requirements
- **GRPO Implementation**: Utilizes Generalized Reinforced Preference Optimization for improved alignment

## Usage Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from vllm import SamplingParams
import torch

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("almaghrabima/ALLaM-Thinking")
model = AutoModelForCausalLM.from_pretrained("almaghrabima/ALLaM-Thinking")

# For faster inference with vLLM
from vllm import LLM, SamplingParams
model = LLM(model="almaghrabima/ALLaM-Thinking")

# Format the prompt using chat template
text = tokenizer.apply_chat_template([
{"role": "user", "content": "في فريق مكون من 15 لاعباً، 40% منهم يسجلون الأهداف. إذا سجل كل لاعب من اللاعبين الذين يسجلون الأهداف في المتوسط 5 أهداف خلال الموسم، فكم عدد الأهداف الكلي التي سجلها اللاعبون الذين يسجلون الأهداف؟"}
], tokenize=False, add_generation_prompt=True)

# Configure sampling parameters
sampling_params = SamplingParams(
temperature=0.8,
top_p=0.95,
max_tokens=1024,
)

# Generate response
output = model.fast_generate(
[text],
sampling_params=sampling_params,
lora_request=None,
)[0].outputs[0].text

print(output)
```

## Unsloth Optimization

This model has been optimized using [Unsloth](https://github.com/unslothai/unsloth), which provides significant speedups for training and inference.

## Training Details

ALLaM-Thinking was trained using a combination of techniques:

- Base architecture fine-tuned on diverse Arabic datasets
- GRPO (Generalized Reinforced Preference Optimization) for better alignment
- Specialized training on mathematical reasoning and step-by-step problem solving

## Performance

ALLaM-Thinking demonstrates strong capabilities in:

- Mathematical problem solving with step-by-step reasoning
- Logical analysis and deduction
- Maintaining coherence in long-form responses
- Domain-specific reasoning in technical fields

## Limitations

- Model outputs should always be verified by human experts, especially for critical applications
- May occasionally produce incorrect mathematical reasoning despite step-by-step approach
- Limited context window compared to some larger models
- Performance may vary based on query complexity and domain specificity

## Citation

If you use ALLaM-Thinking in your research or applications, please cite:

```bibtex


@misc
{almaghrabima2025allam,
author = {Al-Maghrabima Research},
title = {ALLaM-Thinking: Arabic Large Language Model with Enhanced Reasoning Capabilities},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/almaghrabima/ALLaM-Thinking}}
}
```

## License

This model is released under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0).

README.md

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----
-license: apache-2.0
-tags:
-- unsloth
-- trl
-- grpo
----
+---
+license: apache-2.0
+tags:
+- unsloth
+- trl
+- grpo
+---
+