Biggie-SmoLlm-0.15B-Base
Maintainer: nisten
220
✅
| 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
Biggie-SmoLlm-0.15B-Base is a small language model created by maintainer nisten that is a "Frankenstein" of the smolLm-0.13b model upped to 0.15B parameters. This model was created through semi-automated continuous merging to improve its coherence compared to the original smolLm-0.13b model. Similar models include the TinyLlama-1.1B series, which aim to pretrain a 1.1B Llama model on 3 trillion tokens, as well as the SmolLM series of small language models ranging from 135M to 1.7B parameters.
Model inputs and outputs
The Biggie-SmoLlm-0.15B-Base model takes in text prompts and generates corresponding text outputs. The maintainer provides an example of using the llama-cli to generate text based on the prompt "How to build a city on Mars via calculating Aldrin-Cycler orbits?".
Inputs
- Text prompts for the model to generate responses to
Outputs
- Generated text based on the input prompts
Capabilities
The Biggie-SmoLlm-0.15B-Base model is capable of generating coherent text, even at a default temperature setting of 0. The maintainer notes that the model's performance can be further improved by adjusting the temperature and other generation settings. While the model is small at only 150MB, it demonstrates impressive text generation capabilities.
What can I use it for?
The Biggie-SmoLlm-0.15B-Base model could be a useful starting point for further training or fine-tuning on specific tasks. The maintainer suggests it could be an "amazing option for further training" due to its coherent text generation even with minimal tuning. Some potential use cases could include generating educational content, writing assistants, or assisting with research and analysis tasks that require text generation.
Things to try
One interesting aspect of the Biggie-SmoLlm-0.15B-Base model is its small size of only 150MB, yet it still demonstrates the ability to generate coherent text. This suggests the potential for deploying small yet capable language models in resource-constrained environments or on edge devices. Experimenting with different generation settings, fine-tuning the model on specific datasets, or exploring ways to further optimize its performance could yield interesting results.
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-135M
137
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.
Updated Invalid Date
✨
TinyLlama-1.1B-intermediate-step-1431k-3T
147
TinyLlama-1.1B is a 1.1B parameter language model developed by TinyLlama as part of the TinyLlama project. The model aims to pretrrain on 3 trillion tokens over 90 days using 16 A100-40G GPUs. TinyLlama-1.1B adopts the same architecture and tokenizer as the Llama 2 model, allowing it to be used in many open-source projects built upon Llama. Despite its compact size, TinyLlama-1.1B can cater to a variety of applications that require restricted computation and memory footprint. Model inputs and outputs TinyLlama-1.1B is a text-to-text model, taking in natural language prompts as input and generating corresponding text outputs. The model can be used for a wide range of natural language tasks, from open-ended text generation to question answering and task-oriented dialogue. Inputs Natural language prompts of varying length Outputs Generated text continuations, with configurable parameters like length, sampling temperature, and top-k/top-p filtering Capabilities The TinyLlama-1.1B model has shown promising results on a variety of benchmark tasks, including HellaSwag, Obqa, WinoGrande, ARC, boolq, and piqa. As the model is progressively trained on more data, its performance steadily improves, reaching an average score of 52.99 on these tasks after 3 trillion tokens of pretraining. What can I use it for? Given its compact size and strong performance, TinyLlama-1.1B can be utilized in a wide range of applications that demand efficient language models. Some potential use cases include: Generative AI assistants**: The model can be fine-tuned to engage in open-ended conversations, answer questions, and assist with various tasks. Content generation**: TinyLlama-1.1B can be used to generate high-quality text for applications like creative writing, article summarization, and product descriptions. Specialized language models**: The model's modular design allows it to be further customized and fine-tuned for domain-specific tasks, such as scientific writing, legal document processing, or financial analysis. Things to try Experiment with the various hyperparameters of the text generation process, such as temperature, top-k, and top-p, to see how they affect the diversity and coherence of the generated text. You can also explore fine-tuning the model on specialized datasets to enhance its capabilities for your particular use case.
Updated Invalid Date
🛠️
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-135M-Instruct
82
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.
Updated Invalid Date