cosmo-1b
Maintainer: HuggingFaceTB
117
✅
| 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
The cosmo-1b model is a 1.8B parameter language model trained by HuggingFaceTB on a synthetic dataset called Cosmopedia. The training corpus consisted of 30B tokens, 25B of which were synthetic from Cosmopedia, augmented with 5B tokens from sources like AutoMathText and The Stack. The model uses the tokenizer from the Mistral-7B-v0.1 model.
Model Inputs and Outputs
The cosmo-1b model is a text-to-text AI model, meaning it can take textual input and generate textual output.
Inputs
- Text prompts that the model uses to generate new text.
Outputs
- Generated text based on the input prompt.
Capabilities
The cosmo-1b model is capable of generating coherent and relevant text in response to given prompts. While it was not explicitly instruction-tuned, the inclusion of the UltraChat dataset in pretraining allows it to be used in a chat-like format. The model can generate stories, explain concepts, and provide informative responses to a variety of prompts.
What Can I Use It For?
The cosmo-1b model could be useful for various text generation tasks, such as:
- Creative writing: The model can be used to generate stories, dialogues, or creative pieces of text.
- Educational content creation: The model can be used to generate explanations, tutorials, or summaries of concepts.
- Chatbot development: The model's chat-like capabilities could be leveraged to build conversational AI assistants.
Things to Try
Some interesting things to try with the cosmo-1b model include:
- Experimenting with different prompts to see the range of text the model can generate.
- Evaluating the model's performance on specific tasks, such as generating coherent stories or explaining complex topics.
- Exploring the model's ability to handle long-form text generation and maintain consistency over extended passages.
- Investigating the model's potential biases or limitations by testing it on a diverse set of inputs.
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
🛠️
SmolLM-1.7B
133
The SmolLM-1.7B is a state-of-the-art small language model developed by HuggingFaceTB. It is part of the SmolLM series, which includes models with 135M, 360M, and 1.7B parameters. These models were trained on the Cosmo-Corpus, a curated dataset that includes synthetic textbooks, educational Python samples, and web-based educational content. The SmolLM-1.7B model has shown promising results on common sense reasoning and world knowledge benchmarks, performing well compared to other models in its size category. It can be used for a variety of text-to-text generation tasks, leveraging its strong foundation in educational and general knowledge domains. Similar models include the cosmo-1b and the btlm-3b-8k-base models, which also utilize large-scale training datasets to achieve state-of-the-art performance in their respective parameter ranges. Model Inputs and Outputs Inputs The SmolLM-1.7B model accepts text prompts as input, which can be used to generate corresponding text outputs. Outputs The model generates coherent, knowledgeable text continuations based on the provided input prompts. Output lengths can be controlled through various generation parameters, such as maximum length, temperature, and top-k sampling. Capabilities The SmolLM-1.7B model excels at tasks that require strong background knowledge and reasoning abilities, such as answering questions, generating explanations, and producing educational content. It can be used to create engaging educational materials, summarize complex topics, and assist with research and analysis tasks. What Can I Use It For? The SmolLM-1.7B model can be leveraged for a wide range of text-generation use cases, particularly in the education and knowledge-sharing domains. Some potential applications include: Generating educational content, such as explanatory articles, practice questions, and example code snippets Assisting with research and analysis by summarizing key points, generating outlines, and expanding on ideas Enhancing customer service and support by providing knowledgeable responses to inquiries Aiding in the creation of interactive learning materials, virtual tutors, and language-learning tools Things to Try One interesting aspect of the SmolLM-1.7B model is its strong grounding in educational and scientific domains, which enables it to provide detailed and nuanced responses on topics like math, computer science, and natural sciences. Try prompting the model with questions or topics from these areas and see how it leverages its broad knowledge to generate informative and engaging outputs. Additionally, you can experiment with different generation parameters, such as adjusting the temperature or top-k sampling, to explore the model's ability to produce a diverse range of responses while maintaining coherence and relevance.
Updated Invalid Date
📉
SmolLM-360M
50
SmolLM-360M is a state-of-the-art small language model from HuggingFaceTB. Part of the SmolLM series, it is available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on the Cosmo-Corpus, a curated high-quality training dataset that includes synthetic textbooks, educational Python samples, and web content. Compared to other models in their size categories, SmolLM has shown promising results on benchmarks testing common sense reasoning and world knowledge. The similar models, SmolLM-135M and SmolLM-1.7B, offer smaller and larger versions of the same architecture, allowing users to balance performance and resource requirements. Model Inputs and Outputs The SmolLM-360M model takes in text inputs and generates text outputs. It can be used for a variety of natural language processing tasks, such as: Inputs Plain text prompts Conversational messages Outputs Coherent, contextual text responses Continuation of input prompts Answers to questions Capabilities SmolLM-360M can understand and generate text on a wide range of topics, from general knowledge to creative writing and basic programming. It has shown strong performance on tasks like answering questions, summarizing information, and generating coherent narratives. However, the model may struggle with more complex reasoning, mathematical operations, or tasks requiring factual accuracy, as the generated content can sometimes be inconsistent or biased. What Can I Use It For? SmolLM-360M can be a valuable tool for a variety of applications, such as: Content Generation**: Assist with writing tasks like articles, stories, or scripts by providing relevant text suggestions and continuations. Question Answering**: Answer general knowledge questions or provide information on a wide range of topics. Code Generation**: Generate simple, functioning Python code snippets for tasks like printing, variable assignment, and control flow. Conversational AI**: Engage in natural conversations and respond appropriately to user messages. Things to Try Try experimenting with different prompts and temperature/top-p settings to see how the model responds. You can also explore the model's capabilities by asking it to perform various tasks, such as summarizing passages, answering trivia questions, or generating creative story ideas. Remember to critically evaluate the model's outputs and not rely on them as definitive sources of information.
Updated Invalid Date
⛏️
Turkish-Llama-8b-v0.1
48
The Turkish-Llama-8b-v0.1 model is a fully fine-tuned version of the LLaMA-3 8B model with a 30GB Turkish dataset, developed by the COSMOS AI Research Group at Yildiz Technical University. This model is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner. However, due to the diverse nature of the training data, the model can exhibit biases that users should be aware of. Model Inputs and Outputs Inputs Text prompt to continue or build upon Outputs Continued text generated in a coherent and contextually relevant manner Capabilities The Turkish-Llama-8b-v0.1 model can be used for a variety of text generation tasks in Turkish, such as creative writing, summarization, and dialogue generation. The model's fine-tuning on a large Turkish dataset allows it to generate text that is fluent and natural-sounding in the Turkish language. What Can I Use It For? The Turkish-Llama-8b-v0.1 model can be a valuable tool for Turkish language applications and projects, such as: Developing chatbots or virtual assistants that can engage in natural conversations in Turkish Generating Turkish text for creative writing, storytelling, or script development Summarizing longer Turkish text passages into concise summaries Assisting with language learning and practice for Turkish speakers Things to Try One interesting thing to try with the Turkish-Llama-8b-v0.1 model is to explore its ability to generate coherent and contextually relevant text in response to diverse Turkish prompts. You could try providing the model with partial sentences, dialogue snippets, or even just keywords, and see how it continues the text in a natural and logical way. This can help uncover the model's strengths and limitations in understanding and generating Turkish language.
Updated Invalid Date
👨🏫
cosmo-xl
82
cosmo-xl is a conversation agent developed by the Allen Institute for AI (AllenAI) that aims to model natural human conversations. It is trained on two datasets: SODA and ProsocialDialog. The model can accept situation descriptions as well as instructions on the role it should play, and is designed to have greater generalizability on both in-domain and out-of-domain chitchat datasets compared to other models. Model Inputs and Outputs Inputs Situation Narrative**: A description of the situation or context with the characters included (e.g. "David goes to an amusement park") Role Instruction**: An instruction on the role the model should play in the conversation Conversation History**: The previous messages in the conversation Outputs The model generates a continuation of the conversation based on the provided inputs. Capabilities cosmo-xl is designed to engage in more natural and contextual conversations compared to traditional chatbots. It can understand the broader situation and adjust its responses accordingly, rather than just focusing on the literal meaning of the previous message. The model also aims to be more coherent and consistent in its responses over longer conversations. What Can I Use It For? cosmo-xl could be used to power more engaging and lifelike conversational interfaces, such as virtual assistants or chatbots. Its ability to understand context and maintain coherence over longer dialogues makes it well-suited for applications that require more natural language interactions, such as customer service, educational tools, or entertainment chatbots. However, it's important to note that the model was trained primarily for academic and research purposes, and the creators caution against using it in real-world applications or services as-is. The outputs may still contain potentially offensive, problematic, or harmful content, and should not be used for advice or to make important decisions. Things to Try One interesting aspect of cosmo-xl is its ability to take on different roles in a conversation based on the provided instructions. Try giving it various role-playing prompts, such as "You are a helpful customer service agent" or "You are a wise old mentor", and see how it adjusts its responses accordingly. You can also experiment with providing more detailed situation descriptions and observe how the model's responses change based on the context. For example, try giving it a prompt like "You are a robot assistant at a space station, and a crew member is asking you for help repairing a broken module" and see how it differs from a more generic "Help me repair a broken module".
Updated Invalid Date