Hugging Face is a platform that hosts a collection of pretrained and fine-tuned generative text models, including the 70 billion parameter Python specialist version. This model is designed for general code synthesis and understanding.

Model Use:
To use this model, make sure to install the “transformers” package using the following command:
“`bash
pip install transformers accelerate
“`

Model Details:
– Model Developers: Meta
– Variations: Code Llama comes in four model sizes and three variants: base models, Python-specific models, and models for instruction following and safer deployment.
– Input: Model’s input text only.
– Output: Model’s generated text only.
– Model Architecture: Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and does not support long context of up to 100k tokens.

Intended Use:
– Intended Use Cases: Code Llama and its variants are intended for commercial and research use in English and relevant programming languages.
– Out-of-Scope Uses: Use in any manner that violates applicable laws or regulations, use in languages other than English, and any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.

Hardware and Software:
– Training Factors: Custom training libraries were used, and training and fine-tuning were performed on Meta’s Research Super Cluster.
– Carbon Footprint: Training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB. The estimated total emissions were 228.55 tCO2eq.

Evaluation Results:
– See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.

Ethical Considerations and Limitations:
Code Llama and its variants are new technologies that carry risks with use. As with all large language models (LLMs), Code Llama’s potential outputs cannot be predicted in advance. Therefore, developers should perform safety testing and tuning tailored to their specific applications of the model. A Responsible Use Guide is available at https://ai.meta.com/llama/responsible-use-guide.

For more information, including links to other models, please refer to the repository for the 70B Python specialist version in the Hugging Face Transformers format.

Source link
Manual for Hugging Face Code Llama 70B Python Specialist Version

Introduction:
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This manual is specifically for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding.

Model Use:
To use this model, ensure that you have installed the transformers library with the following command:
“`bash
pip install transformers
“`

Model Details:
– Model Developers: Meta
– Variations: Code Llama comes in four model sizes and three variants: base models designed for general code synthesis and understanding, Python-specific model designed for Python, and a model for instruction following and safer deployment. All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.
– Input: Models input text only.
– Output: Models generate text only.
– Model Architecture: Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and does not support long context of up to 100k tokens.
– Model Dates: Code Llama and its variants have been trained between January 2023 and January 2024.
– Status: This is a static model trained on an offline dataset. Future versions of Code Llama – Instruct will be released as improvement is made with community feedback.
– License: A custom commercial license is available at: [Link to License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
– Research Paper: More information can be found in the paper “Code Llama: Open Foundation Models for Code” or its [arXiv page](https://arxiv.org/abs/2308.12950).

Intended Use:
– Intended Use Cases: Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama – Python is designed specifically to handle the Python programming language, and Code Llama – Instruct is intended to be safer to use for code assistant and generation applications.
– Out-of-Scope Uses: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.

Hardware and Software:
– Training Factors: Custom training libraries and performed on Meta’s Research Super Cluster.
– Carbon Footprint: Training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.

Evaluation Results:
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.

Ethical Considerations and Limitations:
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered all scenarios. As with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide) for further guidance.

For more information and access to the model, visit the [Hugging Face Code Llama repository](https://huggingface.co/models).

Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.



Model Use

To use this model, please make sure to install transformers.

pip install transformers accelerate

Model capabilities:



Model Details

*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).

Model Developers Meta

Variations Code Llama comes in four model sizes, and three variants:

  • Code Llama: base models designed for general code synthesis and understanding
  • Code Llama – Python: designed specifically for Python
  • Code Llama – Instruct: for instruction following and safer deployment

All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.

This repository contains the Python version of the 70B parameters model.

Input Models input text only.

Output Models generate text only.

Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant does not support long context of up to 100k tokens.

Model Dates Code Llama and its variants have been trained between January 2023 and January 2024.

Status This is a static model trained on an offline dataset. Future versions of Code Llama – Instruct will be released as we improve model safety with community feedback.

License A custom commercial license is available at: https://ai.meta.com/resources/models-and-libraries/llama-downloads/

Research Paper More information can be found in the paper “Code Llama: Open Foundation Models for Code” or its arXiv page.



Intended Use

Intended Use Cases Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama – Python is designed specifically to handle the Python programming language, and Code Llama – Instruct is intended to be safer to use for code assistant and generation applications.

Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.



Hardware and Software

Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.

Carbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.



Evaluation Results

See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.



Ethical Considerations and Limitations

Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.

Please see the Responsible Use Guide available available at https://ai.meta.com/llama/responsible-use-guide.

The

tag in HTML is a versatile and useful element that is often used to create sections or divisions in a webpage. It can be used in various use cases, including but not limited to:

1. Artificial Intelligence (AI)
The

tag can be used to create sections in a webpage that display information about various artificial intelligence concepts, frameworks, and tools. For example, it can be used to display details about the Code Llama 70B Python specialist version, as well as provide links to other AI models and resources.

2. Python
The

tag can be used to showcase information about Python programming language and its applications. In the context of the Code Llama model, it can be used to display details about the Python variant of the model, including its capabilities and intended use cases.

3. Coding and Frameworks
The

tag can be used to organize and display information about coding, frameworks, and development tools. It can be used to present details about the Model Use, Model Details, Intended Use, and other relevant information related to coding and frameworks.

4. Hugging Face and Creation of AI Models
The

tag can be used to structure and display information about Hugging Face Transformers format, which is used for AI model creation and fine-tuning. It can also be used to create sections for providing information on the architecture, developers, variations, and other details of the AI models.

5. Firebase and Google Cloud
In the context of web development and deployment, the

tag can be used to present information about Firebase, Google Cloud, and other cloud services. It can be used to provide details about the hardware and software used for training the AI models, as well as the carbon footprint and evaluation results associated with the technology.

6. Database and Vector DB
The

tag can be used to structure and present information about databases, such as Vector DB, and their applications in AI and machine learning. It can be used to display details about the training factors, evaluation results, ethical considerations, and limitations of using AI models and databases.

7. Flutter and Dialogflow
The

tag can be used to organize and display information about software development platforms such as Flutter and Dialogflow. It can be used to create sections for providing details about the use cases, intended uses, and ethical considerations related to using AI models and frameworks in software development.

Overall, the

tag is a fundamental element in HTML that can be used to structure and organize information about a wide range of topics and use cases, including those related to artificial intelligence, coding, frameworks, and cloud services.

2024-01-30T18:30:22+01:00