SmolLM-1.7B
Maintainer: HuggingFaceTB
133
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| 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 |
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Model Overview
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-1.7Bmodel 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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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SmolLM-135M is a small language model developed by HuggingFace as part of their SmolLM series. This 135M parameter model is built on the Cosmo-Corpus dataset, which includes high-quality synthetic textbooks, educational Python samples, and web content. Compared to other models in its size category, SmolLM-135M has demonstrated strong performance on common sense reasoning and world knowledge benchmarks. It is available in three sizes - 135M, 360M, and 1.7B parameters - allowing users to choose the model that best fits their needs and resource constraints. Model Inputs and Outputs SmolLM-135M is a causal language model, taking in text prompts and generating continuations. The model accepts text input and returns generated text output. Inputs Text prompt to be continued or built upon Outputs Generated text continuation of the input prompt Capabilities SmolLM-135M can be used for a variety of text generation tasks, such as story writing, question answering, and code generation. The model has been shown to excel at tasks requiring common sense reasoning and world knowledge, making it a useful tool for applications that need to generate coherent and contextually-appropriate text. What Can I Use It For? SmolLM-135M can be fine-tuned or used in prompt engineering for a range of NLP applications, such as: Content Generation**: Generating coherent and contextually-relevant text for things like creative writing, product descriptions, or educational content. Question Answering**: Using the model to generate answers to factual questions based on its broad knowledge base. Code Generation**: Leveraging the model's understanding of programming concepts to generate sample code snippets or complete functions. Things to Try One interesting thing to try with SmolLM-135M is exploring its ability to generate text that exhibits common sense reasoning and an understanding of the world. For example, you could provide the model with a prompt about a specific scenario and see how it continues the story in a logical and plausible way. Alternatively, you could test the model's knowledge by asking it questions about various topics and analyzing the quality of its responses. Another avenue to explore is the model's performance on tasks that require both language understanding and generation, such as summarization or translation. By fine-tuning SmolLM-135M on appropriate datasets, you may be able to create useful and efficient models for these applications.
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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.
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SmolLM-135M-Instruct
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The SmolLM-135M-Instruct model is part of the SmolLM series of small language models developed by HuggingFaceTB. The SmolLM models are built on the Cosmo-Corpus dataset, which includes high-quality synthetic textbooks, educational Python samples, and web content. The models have been instruction tuned using publicly available datasets like WebInstructSub and StarCoder2-Self-OSS-Instruct, and further refined through Direct Preference Optimization. The SmolLM-1.7B-Instruct and SmolLM-135M models are similar in architecture and training, but differ in the number of parameters. The SmolLM-1.7B-Instruct is a larger version with 1.7B parameters, while the SmolLM-135M is a smaller 135M parameter model. Model inputs and outputs The SmolLM-135M-Instruct model takes text as input and generates text as output. It is particularly well-suited for prompts using a chat format, where the input is provided as a user message and the output is the model's response. Inputs Text prompts, often in a chat-like format with user messages Outputs Generated text responses to the input prompts Capabilities The SmolLM-135M-Instruct model has been trained on a diverse dataset and can generate text on a wide variety of topics. It has shown promising results on benchmarks testing common sense reasoning and world knowledge, compared to other models in its size category. What can I use it for? The SmolLM-135M-Instruct model can be used for a range of language-based tasks, such as question answering, text summarization, and content generation. It could be particularly useful for applications that require a small, fast language model with reasonable capabilities, such as chatbots, virtual assistants, or educational tools. Things to try One interesting aspect of the SmolLM-135M-Instruct model is its ability to generate text in response to open-ended prompts, while maintaining a degree of coherence and logical consistency. You could try providing the model with a wide range of prompts, from simple questions to more complex instructions, and observe how it responds. Additionally, you could experiment with different generation parameters, such as temperature and top-p sampling, to see how they affect the model's output.
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