OLMoE-1B-7B-0924-Instruct
Maintainer: allenai
77
🤿
Property | Value |
---|---|
Run this model | Run 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
OLMoE-1B-7B-0924-Instruct
is a Mixture-of-Experts language model with 1 billion active and 7 billion total parameters, released in September 2024. It was adapted from the OLMoE-1B-7B model via supervised fine-tuning and direct preference optimization, yielding state-of-the-art performance among models with a similar cost. The model is 100% open-source and can compete with much larger language models like Llama2-13B-Chat.
Model inputs and outputs
The OLMoE-1B-7B-0924-Instruct
model takes in text-based prompts and generates relevant responses. It supports a variety of input formats, including the chat template format used in the example code.
Inputs
- Text-based prompts, ideally structured in a conversational format
Outputs
- Generated text responses to the input prompts
Capabilities
The OLMoE-1B-7B-0924-Instruct
model demonstrates strong performance on a range of benchmarks, including commonsense reasoning, open-ended question answering, and various other language understanding tasks. It is particularly adept at tasks requiring logical reasoning and inference.
What can I use it for?
The OLMoE-1B-7B-0924-Instruct
model can be used for a variety of natural language processing applications, such as building conversational assistants, generating informative content, and aiding in research and development. Its strong performance and open-source availability make it an attractive option for both commercial and academic use cases.
Things to try
One interesting aspect of the OLMoE-1B-7B-0924-Instruct
model is its ability to engage in multi-turn conversations, maintaining context and coherence over longer exchanges. Developers could experiment with using the model in interactive chatbot applications, observing how it responds to follow-up questions and requests for clarification or additional detail.
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
🧪
OLMoE-1B-7B-0924
100
The OLMoE-1B-7B-0924 is a Mixture-of-Experts (MoE) language model developed by allenai. It has 1 billion active parameters and 7 billion total parameters, and was released in September 2024. The model yields state-of-the-art performance among models with a similar cost (1B) and is competitive with much larger models like Llama2-13B. OLMoE is 100% open-source. Similar models include the OLMo-7B-0424 from allenai, which is a 7 billion parameter version of the OLM model released in April 2024. There is also the OLMo-Bitnet-1B from NousResearch, which is a 1 billion parameter model trained using 1-bit techniques. Model inputs and outputs Inputs Raw text to be processed by the language model Outputs Continued text generation based on the input prompt Embeddings or representations of the input text that can be used for downstream tasks Capabilities The OLMoE-1B-7B-0924 model is capable of generating coherent and contextual text continuations, answering questions, and performing other natural language understanding and generation tasks. For example, given the prompt "Bitcoin is", the model can generate relevant text continuing the sentence, such as "Bitcoin is a digital currency that is created and held electronically. No one controls it. Bitcoins arent printed, like dollars or euros theyre produced by people and businesses running computers all around the world, using software that solves mathematical". What can I use it for? The OLMoE-1B-7B-0924 model can be used for a variety of natural language processing applications, such as text generation, dialogue systems, summarization, and knowledge-based question answering. For companies, the model could be fine-tuned and deployed in customer service chatbots, content creation tools, or intelligent search and recommendation systems. Researchers could also use the model as a starting point for further fine-tuning and investigation into language model capabilities and behavior. Things to try One interesting aspect of the OLMoE-1B-7B-0924 model is its Mixture-of-Experts architecture. This allows the model to leverage specialized "experts" for different types of language tasks, potentially improving performance and generalization. Developers could experiment with prompts that target specific capabilities, like math reasoning or common sense inference, to see how the model's different experts respond. Additionally, the open-source nature of the model enables customization and further research into language model architectures and training techniques.
Updated Invalid Date
🔮
OLMo-7B-Instruct
50
The OLMo-7B-Instruct is an AI model developed by the research organization allenai. It is a text-to-text model, meaning it can generate text outputs based on text inputs. While the platform did not provide a detailed description of this specific model, it shares some similarities with other models in the OLMo and LLaMA model families, such as OLMo-7B and LLaMA-7B. Model inputs and outputs The OLMo-7B-Instruct model takes text-based inputs and generates text-based outputs. The specific inputs and outputs can vary depending on the task or application it is used for. Inputs Text-based prompts or instructions Outputs Generated text based on the input prompts Capabilities The OLMo-7B-Instruct model has the capability to generate human-like text based on the provided inputs. This can be useful for a variety of natural language processing tasks, such as content generation, question answering, and task completion. What can I use it for? The OLMo-7B-Instruct model can be used for a wide range of text-based applications, such as creating content for blogs, articles, or social media posts, generating responses to customer inquiries, or assisting with task planning and execution. It can also be fine-tuned or combined with other models to create more specialized applications. Things to try With the OLMo-7B-Instruct model, you can experiment with different types of text-based inputs and prompts to see the variety of outputs it can generate. You can also explore ways to integrate the model into your existing workflows or applications to automate or enhance your text-based tasks.
Updated Invalid Date
👀
SmolLM-1.7B-Instruct
89
SmolLM-1.7B-Instruct is a state-of-the-art small language model developed by HuggingFaceTB. It is part of the SmolLM series, which includes three model sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a high-quality training dataset that includes synthetic textbooks, educational Python samples, and web-based educational content. The SmolLM-1.7B-Instruct model was further fine-tuned using publicly available instruction datasets, such as WebInstructSub and StarCoder2-Self-OSS-Instruct, to enable better instruction following capabilities. The model was also optimized using Direct Preference Optimization (DPO) techniques to align its outputs with human preferences. Compared to similar models like Mixtral-8x7B-Instruct-v0.1 and llama-3-8b-Instruct, the SmolLM-1.7B-Instruct model offers a more compact size while maintaining strong performance on a variety of benchmarks. Model inputs and outputs Inputs Text prompts**: The model accepts text-based prompts as input, which can include instructions, questions, or other types of requests. Outputs Generated text**: The model generates relevant and coherent text in response to the input prompt. This can include answers to questions, step-by-step instructions, or other types of informative or creative content. Capabilities The SmolLM-1.7B-Instruct model excels at a wide range of text-based tasks, including question answering, task completion, and creative writing. It demonstrates strong reasoning and language understanding capabilities, making it suitable for applications that require intelligent text generation. What can I use it for? The SmolLM-1.7B-Instruct model can be useful for a variety of applications, such as: Intelligent assistants**: The model can be integrated into chatbots or virtual assistants to provide helpful and informative responses to user queries. Content generation**: The model can be used to generate high-quality text for blog posts, articles, or other types of written content. Educational applications**: The model's understanding of educational concepts and ability to provide step-by-step instructions makes it suitable for developing interactive learning tools or automated tutoring systems. Things to try One interesting thing to try with the SmolLM-1.7B-Instruct model is exploring its ability to follow complex multi-step instructions. For example, you could prompt the model with a request to bake a cake from scratch and see how it responds, providing detailed steps and guidance. Another interesting area to explore is the model's capacity for logical reasoning and problem-solving, which can be tested through prompts that involve math, coding, or other analytical tasks.
Updated Invalid Date
🔮
OLMo-Bitnet-1B
105
OLMo-Bitnet-1B is a 1 billion parameter language model trained using the One Bit Large Model (OLMo) method described in the paper The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits. It was trained on the first 60 billion tokens of the Dolma dataset, making it a research proof-of-concept to test the OLMo methodology. The model can be compared to the bitnet_b1_58-3B model, which is a reproduction of the BitNet b1.58 paper. Both models leverage the 1-bit encoding approach to significantly reduce the memory footprint while maintaining competitive performance. Model inputs and outputs The OLMo-Bitnet-1B model is a text-to-text language model, which means it can be used to generate or manipulate text based on an input prompt. Inputs Text prompt**: A string of text that the model uses to generate or transform additional text. Outputs Generated text**: The text produced by the model in response to the input prompt. Capabilities The OLMo-Bitnet-1B model can be used for a variety of text-based tasks, such as language generation, text summarization, and text translation. The model's smaller size and efficient encoding make it suitable for deployment on resource-constrained devices. What can I use it for? The OLMo-Bitnet-1B model can be fine-tuned or used as a starting point for various natural language processing applications, such as: Content generation**: Generating coherent and contextually relevant text for tasks like creative writing, article generation, or chatbots. Language modeling**: Evaluating and improving language models by using the OLMo-Bitnet-1B as a baseline or fine-tuning it on specific datasets. Transfer learning**: Using the OLMo-Bitnet-1B as a foundation model to kickstart the training of more specialized models for tasks like sentiment analysis, question answering, or text classification. Things to try One interesting aspect of the OLMo-Bitnet-1B model is its efficient 1-bit encoding, which allows it to have a smaller memory footprint compared to traditional language models. This makes it a good candidate for deployment on devices with limited resources, such as edge devices or mobile phones. To explore the model's capabilities, you could try: Deploying the model on a resource-constrained device**: Experiment with quantizing the model to 4-bit or 8-bit precision to further reduce its memory requirements and evaluate its performance. Fine-tuning the model on a specific dataset**: Adapt the OLMo-Bitnet-1B to a particular domain or task by fine-tuning it on a relevant dataset, and compare its performance to other language models. Exploring the model's out-of-distribution performance**: Test the model's ability to generalize to unseen or unusual inputs, and investigate its robustness to distributional shift. By exploring the OLMo-Bitnet-1B model in these ways, you can gain insights into the potential of 1-bit encoding for efficient and accessible language modeling.
Updated Invalid Date