Open-Assistant StableLM SFT-7 (7B)
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Open-Assistant StableLM SFT-7 (7B)

Open-source 7B parameter language model for diverse NLP tasks.

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Open-Assistant StableLM SFT-7 (7B)

Open-Assistant StableLM SFT-7 (7B) API is an open-source large language model with 7 billion parameters for various natural language processing applications.

Open-Assistant StableLM SFT-7 (7B)

Model Overview Card for Open-Assistant StableLM SFT-7 (7B)

Basic Information

Model Name: Open-Assistant StableLM SFT-7 (7B)

Developer/Creator: Open-Assistant

Release Date: April 2023

Version: 1.0

Model Type: Large Language Model (LLM)


Overview:Open-Assistant StableLM SFT-7 (7B) is an open-source large language model designed to assist with various natural language processing tasks. It is based on the StableLM architecture and has been fine-tuned using supervised fine-tuning (SFT) techniques.

Key Features:
  • 7 billion parameters
  • Open-source and freely available
  • Fine-tuned using supervised learning
  • Capable of generating human-like text responses
  • Supports multiple languages
Intended Use:

The model is intended for a wide range of natural language processing tasks, including but not limited to:

  • Text generation
  • Question answering
  • Summarization
  • Language translation
  • Code generation and analysis
Language Support:

While specific language support information is not provided, large language models of this scale typically support multiple languages, with a focus on English and other widely-spoken languages.

Technical Details


Open-Assistant StableLM SFT-7 (7B) is based on the transformer architecture, which has become the standard for large language models. The model likely uses a decoder-only transformer architecture, similar to GPT models.

Training Data:

Specific details about the training data are not provided in the available information. However, as an open-source model developed by LAION and Stability AI, it is likely trained on a diverse dataset of web-crawled text, books, and other publicly available sources.

Data Source and Size:

The exact size of the training dataset is not specified, but given the model's 7 billion parameters, it is likely trained on a dataset in the range of hundreds of gigabytes to a few terabytes of text data.

Knowledge Cutoff:

The knowledge cutoff date for this model is not explicitly stated. However, given its release date in April 2023, it is reasonable to assume that its knowledge cutoff is sometime in late 2022 or early 2023.

Diversity and Bias:

Without specific information about the training data, it's challenging to assess the diversity and potential biases of the model. However, as an open-source project, efforts may have been made to address bias and improve diversity in the training data.

Performance Metrics:

Detailed performance metrics for the Open-Assistant StableLM SFT-7 (7B) model are not provided in the available information. However, typical metrics for language models of this size include:

  • Perplexity: A measure of how well the model predicts a sample of text. Lower values indicate better performance.
  • BLEU score: Used for evaluating machine translation quality.
  • ROUGE score: Used for evaluating text summarization quality.
  • F1 score: A measure of accuracy for classification tasks.

Inference speed for a 7 billion parameter model can vary depending on the hardware used. On modern GPUs, inference times for generating responses are typically in the range of milliseconds to a few seconds, depending on the length of the output.Robustness:The model's robustness across different topics and languages would depend on the diversity of its training data. As a 7 billion parameter model, it is likely to have good generalization capabilities, but specific performance across diverse inputs would require further testing and evaluation.

Code Sample

Unfortunately, specific usage information for the Open-Assistant StableLM SFT-7 (7B) model is not available in the provided search results. However, as an open-source model, it is likely that the model can be accessed through popular machine learning frameworks such as PyTorch or TensorFlow.

Ethical Guidelines:

While specific ethical guidelines for this model are not provided, it is important for users to consider general AI ethics principles when using large language models. These may include:

  • Avoiding the generation of harmful or biased content
  • Respecting copyright and intellectual property rights
  • Ensuring transparency about the use of AI-generated content
  • Protecting user privacy when processing personal data
License Type:

The specific license for the Open-Assistant StableLM SFT-7 (7B) model is not mentioned in the available information. However, as an open-source project, it is likely released under a permissive open-source license such as MIT, Apache, or Creative Commons.

Open-Assistant StableLM SFT-7 (7B)

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