xgen-7b-8k-inst
Maintainer: Salesforce
95
🌿
| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
Create account to get full access
Model overview
xgen-7b-8k-inst is a large language model developed by Salesforce AI Research. It is part of the XGen family of models, which are trained on up to 8K sequence lengths to enable better performance on long-form tasks like summarization and knowledge-based question answering. The xgen-7b-8k-inst model is the instruction-finetuned version, adapted for tasks that require the model to follow specific prompts or guidelines.
Compared to similar models like XVERSE-13B and CodeGen-16B-Multi, the xgen-7b-8k-inst has a smaller parameter count (7 billion) but a longer input sequence length, making it well-suited for tasks that benefit from longer context. The XVERSE-13B model, for example, is a larger but more general-purpose language model, while the CodeGen models are specialized for programming-related tasks.
Model inputs and outputs
Inputs
- Raw text data, which can include natural language, code, or a mix of both
- The model accepts input sequences up to 8,192 tokens long, allowing it to handle long-form content effectively
Outputs
- Autoregressive text completions, generated token-by-token based on the provided input
- The model can output text continuations, answer questions, summarize content, and perform other language generation tasks
Capabilities
The xgen-7b-8k-inst model has shown strong performance on a variety of natural language understanding and generation benchmarks, including question answering, logical reasoning, and mathematical problem-solving. Its ability to handle longer input sequences makes it particularly well-suited for tasks that require maintaining and reasoning over extended context, such as multi-step problem-solving or long-form summarization.
What can I use it for?
The xgen-7b-8k-inst model can be fine-tuned and applied to a wide range of language-related tasks, such as:
- Content generation: Producing high-quality, coherent text continuations for articles, stories, or other long-form content
- Question answering: Answering complex, multi-part questions by drawing on extended context
- Summarization: Generating concise summaries of long documents or articles
- Code generation: Producing code snippets or entire programs based on natural language descriptions
Additionally, the model's instruction-following capabilities make it well-suited for applications that require following specific guidelines or prompts, such as:
- Creative writing: Generating stories or poems based on user-provided prompts
- Technical writing: Drafting technical documentation or tutorials based on outlines or guidelines
- Data analysis: Automating the generation of reports or insights based on structured data
Things to try
One interesting aspect of the xgen-7b-8k-inst model is its ability to maintain and reason over extended context. You could try feeding it a long, multi-paragraph passage and asking it to answer a complex, multi-part question that requires synthesizing information from across the entire text. Its performance on these types of tasks can showcase its strengths in areas like reading comprehension and logical reasoning.
Another interesting experiment would be to try the model on code generation or translation tasks, leveraging its ability to handle longer input sequences. You could provide it with a partially-completed code snippet and ask it to fill in the missing pieces, or give it a natural language description of a programming task and see how it performs at translating that into working code.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🔎
xgen-7b-8k-base
315
XGen-7B-8K-Base is a large language model developed by Salesforce AI Research. It is part of the XGen family of models, which are trained on long sequences of up to 8,000 tokens. The XGen-7B-8K-Base model has 7 billion parameters and is pre-trained on a large corpus of text data. The XGen models are designed for tasks that require processing long input sequences, such as multi-turn conversations, question-answering, and summarization. The 8,000 token context length allows the model to maintain coherence and capture long-range dependencies in the input. This makes the XGen-7B-8K-Base model more versatile compared to models with shorter input lengths. Salesforce has also released an instruction-finetuned version of the model, called XGen-7B-8K-Inst, which is tailored for following instructions and generating helpful, informative responses. Model inputs and outputs Inputs The model accepts text inputs of up to 8,000 tokens. The input can be in the form of a prompt, question, or partially-generated text that the model should continue. Outputs The model generates text continuations, completing the input prompt or answering the input question. The output length can be controlled by specifying a maximum number of new tokens to generate. Capabilities The XGen-7B-8K-Base model is capable of understanding and generating long-form text across a variety of domains. It can be used for tasks like multi-turn dialogue, question-answering, summarization, and open-ended text generation. The long context length allows the model to maintain coherence and consistency over longer inputs. For example, the model could be used to engage in an extended conversation, maintaining the flow and context over many turns. It could also summarize long documents or articles, capturing the key points and high-level structure. Additionally, the model could be used to generate detailed, coherent responses to open-ended questions on a wide range of topics. What can I use it for? The XGen-7B-8K-Base model could be used in a variety of applications that involve processing and generating long-form text. Some potential use cases include: Conversational AI**: Powering chatbots and virtual assistants that can engage in multi-turn dialogues with users. Question-answering**: Building systems that can provide detailed, contextual answers to complex questions. Summarization**: Automatically summarizing long documents, articles, or reports to extract the key information. Content generation**: Generating coherent, long-form text for applications like creative writing, content creation, or storytelling. When using the XGen-7B-8K-Base model, it's important to keep in mind its limitations and potential biases. As with any large language model, the outputs may contain inaccuracies, biases, or inappropriate content. It's recommended to carefully evaluate the model's performance and behavior for specific use cases before deploying it in production. Things to try One interesting aspect of the XGen-7B-8K-Base model is its ability to maintain coherence and consistency over long input sequences. You could try providing the model with a complex, multi-part prompt and see how it continues the text, ensuring that the output is logically consistent and flows naturally from the initial input. Another interesting experiment would be to explore the model's capabilities in open-ended, creative tasks. You could provide the model with a high-level topic or scenario and see how it generates detailed, imaginative responses that build upon the initial prompt. Additionally, you could investigate the model's performance on specific domains or tasks, such as question-answering on specialized subjects or summarizing technical documents. By testing the model's capabilities in various contexts, you can better understand its strengths, limitations, and potential applications.
Updated Invalid Date
🎲
xgen-mm-phi3-mini-instruct-r-v1
143
xgen-mm-phi3-mini-instruct-r-v1 is a series of foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research. This model advances upon the successful designs of the BLIP series, incorporating fundamental enhancements that ensure a more robust and superior foundation. The pretrained foundation model, xgen-mm-phi3-mini-base-r-v1, achieves state-of-the-art performance under 5 billion parameters and demonstrates strong in-context learning capabilities. The instruct fine-tuned model, xgen-mm-phi3-mini-instruct-r-v1, also achieves state-of-the-art performance among open-source and closed-source Vision-Language Models (VLMs) under 5 billion parameters. Model inputs and outputs The xgen-mm-phi3-mini-instruct-r-v1 model is designed for image-to-text tasks. It takes in images and generates corresponding textual descriptions. Inputs Images**: The model can accept high-resolution images as input. Outputs Textual Descriptions**: The model generates textual descriptions that caption the input images. Capabilities The xgen-mm-phi3-mini-instruct-r-v1 model demonstrates strong performance in image captioning tasks, outperforming other models of similar size on benchmarks like COCO, NoCaps, and TextCaps. It also shows robust capabilities in open-ended visual question answering on datasets like OKVQA and TextVQA. What can I use it for? The xgen-mm-phi3-mini-instruct-r-v1 model can be used in a variety of applications that involve generating textual descriptions from images, such as: Image captioning**: Automatically generate captions for images to aid in indexing, search, and accessibility. Visual question answering**: Develop applications that can answer questions about the content of images. Image-based task automation**: Build systems that can understand image-based instructions and perform related tasks. The model's state-of-the-art performance and efficiency make it a compelling choice for Salesforce's customers looking to incorporate advanced computer vision and language capabilities into their products and services. Things to try One interesting aspect of the xgen-mm-phi3-mini-instruct-r-v1 model is its support for flexible high-resolution image encoding with efficient visual token sampling. This allows the model to generate high-quality, detailed captions for a wide range of image sizes and resolutions. Developers could experiment with feeding the model images of different sizes and complexities to see how it handles varied input and generates descriptive outputs. Additionally, the model's strong in-context learning capabilities suggest it may be well-suited for few-shot or zero-shot learning tasks, where the model can adapt to new scenarios with limited training data. Trying prompts that require the model to follow instructions or reason about unfamiliar concepts could be a fruitful area of exploration.
Updated Invalid Date
🤯
codegen-16B-mono
116
CodeGen is a family of autoregressive language models for program synthesis from Salesforce. The checkpoint included in this repository is denoted as CodeGen-Mono 16B, where "Mono" means the model is initialized with CodeGen-Multi 16B and further pre-trained on a Python programming language dataset, and "16B" refers to the number of trainable parameters. Similarly, there is a codegen-350M-mono model with 350M parameters. Additionally, the codegen-16B-multi model is pre-trained on a dataset of multiple programming languages including C, C++, Go, Java, JavaScript, and Python. Another related model is CodeT5+, which is a new family of open-code large language models with an encoder-decoder architecture that can operate in different modes to support a wide range of code understanding and generation tasks. The codet5p-16b and instructcodet5p-16b checkpoints are 16B parameter versions of CodeT5+ that are instruction-tuned for aligning with natural language prompts. Lastly, the codegen25-7b-multi_P model is part of the CodeGen2.5 family, which is a smaller but highly capable model trained on the StarCoderData dataset. This model supports multiple programming languages and can perform both code generation and infilling. Model inputs and outputs Inputs Natural language prompts**: The models are designed to generate code from natural language descriptions, where the prompts should be in the form of a comment string. Partially-generated code**: The models can also complete partially-generated code. Outputs Executable code**: The primary output of these models is executable code in various programming languages, generated based on the input prompts. Capabilities These CodeGen and CodeT5+ models are highly capable at program synthesis, i.e. generating executable code given natural language prompts. They have been shown to outperform many large language models on a variety of code generation benchmarks, even surpassing closed-source models in some cases. The models can handle a diverse set of programming languages, and the multi-lingual variants are able to generate code in multiple languages. They also have the ability to complete partially-generated code, making them useful for code editing and autocompletion tasks. Additionally, the CodeT5+ models are designed to be flexible, supporting different modes of operation (encoder-only, decoder-only, encoder-decoder) to handle a wide range of code understanding and generation tasks. What can I use it for? These models are well-suited for a variety of applications that involve generating or understanding code, such as: Code generation**: Automatically generating code from natural language descriptions, which can be helpful for prototyping, automating repetitive tasks, or assisting developers. Code completion**: Completing partially-written code, which can boost developer productivity. Code understanding**: The CodeT5+ models can be used for various code understanding tasks like code search, code summarization, and code translation. By leveraging the capabilities of these models, developers and researchers can build applications that automate or assist with various programming-related tasks, potentially boosting productivity and expanding the reach of AI in software development. Things to try One interesting aspect of these models is their ability to generate code in multiple programming languages. You could try providing prompts that mix natural language and code snippets in different languages, and see how the models handle the cross-lingual generation. Another interesting exercise would be to explore the models' few-shot or zero-shot capabilities on specific programming tasks or benchmarks. By fine-tuning or prompting the models in creative ways, you may be able to unlock new use cases that go beyond the standard code generation tasks. Finally, you could experiment with the different variants of these models (e.g., codegen-16B-mono vs. codegen-16B-multi) to understand how the pretraining data and model architecture choices impact the models' performance and capabilities.
Updated Invalid Date
🛠️
codegen-16B-multi
119
The codegen-16B-multi is a large autoregressive language model developed by Salesforce for the task of program synthesis. It was initialized with the codegen-nl-16b model and further pre-trained on a dataset of multiple programming languages, including C, C++, Go, Java, JavaScript, and Python, totaling 119.2B tokens. The model uses cross-entropy loss to maximize the likelihood of sequential inputs and was trained using multiple TPU-v4-512 devices. Similar models include CodeGen2.5-7B-multi, a smaller but capable program synthesis model also developed by Salesforce, and the StarCoder and StarCoderBase models from BigCode, which were trained on a broader set of 80+ programming languages. Model inputs and outputs The codegen-16B-multi model takes natural language and programming language text as input and generates executable code. It can complete partially-generated code as well, making it useful for tasks like code autocompletion. Inputs Natural language prompts or comments related to the desired code Partially-generated code snippets Outputs Executable code in a variety of programming languages, including C, C++, Go, Java, JavaScript, and Python Capabilities The codegen-16B-multi model is capable of generating high-quality, executable code in multiple programming languages based on natural language prompts. It can understand the context and intent behind text-based instructions and translate that into functional code. The model has been shown to perform well on benchmarks like HumanEval and MTPB, demonstrating its prowess at program synthesis. What can I use it for? The codegen-16B-multi model can be a powerful tool for developers, engineers, and data scientists who need to quickly generate code for a wide range of applications. Some potential use cases include: Automating repetitive coding tasks Generating boilerplate code or scaffolding Prototyping new ideas and concepts Assisting with programming education and learning By leveraging the model's understanding of natural language and programming constructs, users can save time and increase their productivity when working on software projects. Things to try One interesting aspect of the codegen-16B-multi model is its ability to infill or complete partially-generated code. This can be a useful feature for tasks like code autocompletion, where the model can suggest the next logical step in a programming workflow. To experiment with this, you can try providing the model with a code snippet that has a gap or missing section, and see how it fills in the blank. Another thing to explore is the model's performance on different programming languages. While the model was trained on a diverse set of languages, it may exhibit varying levels of proficiency across them. You can try prompting the model with tasks in different languages and observe how it responds.
Updated Invalid Date