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  # Model Overview
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  ## Description:
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- CodeLlama-13B-QML is a large language model customized by the Qt Company for Fill-In-The-Middle code completion tasks in the QML programming language, especially for Qt Quick Controls compliant with Qt 6 releases. The CodeLlama-13B-QML model is designed for companies and individuals that want to self-host their LLM for HMI (Human Machine Interface) software development instead of relying on third-party hosted LLMs.
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- This model reaches a score of 79% on the QML100 Fill-In-the-Middle code completion benchmark for Qt 6-compliant code. In comparison, CodeLlama-7B-QML (finetuned model from Qt) scored 79%, Claude 3.7 Sonnet scored 76%, Claude 3.5 Sonnet scored 68%, the base CodeLlama-13B scored 66%, GPT-4o scored 62%, the base CodeLlama-7B scored 61%. This model was fine-tuned based on raw data from over 4000 human-created QML code snippets using the LoRa fine-tuning method. CodeLlama-13B-QML is not optimised for the creation of Qt5-release compliant, C++, or Python code.
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  ## Terms of use:
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  By accessing this model, you are agreeing to the Llama 2 terms and conditions of the [license](https://github.com/meta-llama/llama/blob/main/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama/blob/main/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/). By using this model, you are furthermore agreeing to the [Qt AI Model terms & conditions](https://www.qt.io/terms-conditions/ai-services/model-use).
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  ## Usage:
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- CodeLlama-13B-QML is a medium-sized Language Model that requires significant computing resources to perform with inference (response) times suitable for automatic code completion. Therefore, it should be used with a GPU accelerator, either in the cloud environment such as AWS, Google Cloud, Microsoft Azure, or locally.
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  Large Language Models, including CodeLlama-13B-QML, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building AI systems.
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  # Model Overview
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  ## Description:
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+ CodeLlama-13B-QML is a large language model customized by the Qt Company for Fill-In-The-Middle code completion tasks in the QML programming language, especially for Qt Quick Controls compliant with Qt 6 releases. The CodeLlama-13B-QML model is designed for companies and individuals who want to self-host their LLM for HMI (Human Machine Interface) software development instead of relying on third-party hosted LLMs.
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+ This model reaches a score of 86% on the QML100 Fill-In-the-Middle code completion benchmark for Qt 6-compliant code. In comparison, CodeLlama-7B-QML (finetuned model from Qt) scored 79%, Claude 3.7 Sonnet scored 76%, Claude 3.5 Sonnet scored 68%, the base CodeLlama-13B scored 66%, GPT-4o scored 62%, and CodeLlama-7B scored 61%. This model was fine-tuned based on raw data from over 5000 human-created QML code snippets using the LoRa fine-tuning method. CodeLlama-13B-QML is not optimised for the creation of Qt5-release compliant, C++, or Python code.
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  ## Terms of use:
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  By accessing this model, you are agreeing to the Llama 2 terms and conditions of the [license](https://github.com/meta-llama/llama/blob/main/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama/blob/main/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/). By using this model, you are furthermore agreeing to the [Qt AI Model terms & conditions](https://www.qt.io/terms-conditions/ai-services/model-use).
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  ## Usage:
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+ CodeLlama-13B-QML is a medium-sized Language Model that requires significant computing resources to perform with inference (response) times suitable for automatic code completion. Therefore, it should be used with a GPU accelerator, either in a cloud environment such as AWS, Google Cloud, Microsoft Azure, or locally.
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  Large Language Models, including CodeLlama-13B-QML, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building AI systems.
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