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ALLaM-2-7b

allam-2-7b
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TOKEN SPEED
~1800 TPS
Powered bygroq
INPUT
Text
OUTPUT
Text
CAPABILITIES
JSON Object Mode
SDAIA logoSDAIA
Model card

ALLaM-2-7B is a powerful bilingual language model designed to advance Arabic Language Technology (ALT), developed by the National Center for Artificial Intelligence (NCAI) at the Saudi Data and AI Authority (SDAIA). This instruction-tuned model is trained from scratch using a unique two-step pretraining recipe: 4T English tokens followed by 1.2T mixed Arabic/English tokens. This approach retains English capabilities without catastrophic forgetting while effectively transferring knowledge between language distributions, making it ideal for Arabic and English conversational applications.

PRICING

Input
Pending
Output
Pending

LIMITS

CONTEXT WINDOW
4,096
MAX OUTPUT TOKENS
4,096

QUANTIZATION

This uses Groq's TruePoint Numerics, which reduces precision only in areas that don't affect accuracy, preserving quality while delivering significant speedup over traditional approaches. Learn more here.

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

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

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:

shell
pip install groq
Python
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="allam-2-7b",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)

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