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Pharia-1-LLM-7B

by Aleph Alpha

AI Model
Low Confidence

Pharia-1-LLM-7B is a 7-billion-parameter language model developed by Aleph Alpha, designed to deliver transparent, compliant, and domain-specific AI solutions. It is an autoregressive, causal, decoder-only transformer model with rotary position embeddings, optimized for tasks such as text generation, classification, summarization, question answering, and labeling. The model excels in domain-specific applications, particularly in industries like automotive and engineering, thanks to its improved token efficiency and concise, length-controlled responses. It is available in two variants: Pharia-1-LLM-7B-control: An instruction-tuned model without preference alignment or additional safety training, designed for users who prioritize direct control and responsiveness to specific instructions. However, this variant may produce more generic or verbose answers without the fine-tuning of alignment methods. Pharia-1-LLM-7B-control-aligned: This version includes additional alignment training to enhance safety and mitigate risks, making it more suitable for secure conversational applications while potentially reducing responsiveness to specific instructions. Both variants are designed to be deployed in cloud or on-premise environments and are available under the Open Aleph License for educational and research purposes. They support multi-turn interactions and are optimized for performance in the 7B to 8B parameter range, offering a balance between efficiency and customization for critical applications

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Transparency Score

Weighted across four pillars · updated May 15, 2026

Overall grade
52% weighted
ABCDF
Model
—
Not yet assessed
Infrastructure
—
Not yet assessed
Openness Assessment
54%Partially Open
4 Open
7 Partial
3 Closed
Availability
5/5 Open
Documentation
3/6 Open
Access Methods
3/3 Open
Data from EU Open Source AI Index (DOI: 10.5281/zenodo.15386042), licensed under CC-BY 4.0

Capabilities

Text Generation
Summarization
Translation
Question Answering

Vendor Information

Complete information about the vendor/provider of this AI application

Aleph AlphaView all products →
Aleph Alpha GmbH(Trading as: Aleph Alpha)
Contact Information
aleph-alpha.com
info@aleph-alpha.com
Registered Address
Speyerer Straße 14, Heidelberg, Baden-Württemberg, 69123, DE

EU AI Act Provider Information

Verification Status:
Verified
Aleph Alpha GmbH(Trading as: Aleph Alpha)
Speyerer Straße 14, Heidelberg, Baden-Württemberg, 69123, DE
info@aleph-alpha.com
Aleph Alpha GmbH
Speyerer Straße 14, Heidelberg, DE
Compliance Documents
CE Marking: Not Applicable
Available in EU Member States
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Aleph Alpha’s platform enables continuous monitoring and iterative improvements through transparent interactions. Users can review AI-driven processes, collaborate with domain experts, and refine workflows based on explainable outputs. The focus is on maintaining compliance and adaptability in regulated industries, with ongoing updates to enhance performance and fairness.
Aleph Alpha’s AI systems, while designed for transparency and compliance, come with inherent limitations and risks. The customization and integration of their models can be complex and resource-intensive, requiring specialized expertise to adapt solutions to specific organizational needs. Performance may vary depending on the availability and quality of domain-specific data, which could pose challenges in environments with limited data or stringent regulatory requirements. Additionally, the computational demands of advanced AI features may limit scalability for organizations with constrained resources. Users must also consider potential risks related to data privacy and security, particularly when operating in highly regulated industries. While the Pharia stack provides tools for explainability and oversight, the responsibility for validating AI outputs and managing risks ultimately lies with the user, necessitating robust governance and continuous monitoring to ensure compliance and mitigate potential issues
Compliance & Risk

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Added: January 26, 2026
Updated: May 15, 2026

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