### Key Technical Specifications

* Model Architecture
  ALLaM-2-7B is an autoregressive transformer with 7 billion parameters, specifically designed for bilingual Arabic-English applications. The model is pretrained from scratch using a two-step approach that first trains on 4T English tokens, then continues with 1.2T mixed Arabic/English tokens. This unique training methodology preserves English capabilities while building strong Arabic language understanding, making it one of the most capable Arabic LLMs available.

* Performance Metrics
  ALLaM-2-7B demonstrates exceptional performance across Arabic and English benchmarks:
  * MMLU English (0-shot): 63.65% accuracy
  * Arabic MMLU (0-shot): 69.15% accuracy
  * ETEC Arabic (0-shot): 67.0% accuracy
  * IEN-MCQ: 90.8% accuracy
  * MT-bench Arabic Average: 6.6/10
  * MT-bench English Average: 7.14/10


### Model Use Cases

#### Arabic Language Technology
Specifically designed for advancing Arabic language applications:
* Arabic conversational AI and chatbot development
* Bilingual Arabic-English content generation
* Arabic text summarization and analysis
* Cultural context-aware responses for Arabic markets

#### Research and Development
Perfect for Arabic language research and educational applications:
* Arabic NLP research and experimentation
* Bilingual language learning tools
* Arabic knowledge exploration and Q&A systems
* Cross-cultural communication applications


### Model Best Practices
* Leverage bilingual capabilities: Take advantage of the model's strong performance in both Arabic and English for cross-lingual applications
* Use appropriate system prompts: The model works without a predefined system prompt but benefits from custom prompts like 'You are ALLaM, a bilingual English and Arabic AI assistant'
* Consider cultural context: The model is designed with Arabic cultural alignment in mind - leverage this for culturally appropriate responses
* Optimize for context length: Work within the 4K context window for optimal performance
* Apply chat template: Use the model's built-in chat template accessed via apply_chat_template() for best conversational results


### Get Started with ALLaM-2-7B
Experience the capabilities of `allam-2-7b` with Groq speed:

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## Deepseek R1 Distill Llama 70b: Model (tsx)