Elinas
Models by this creator
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alpaca-30b-lora-int4
69
The alpaca-30b-lora-int4 model is a 30 billion parameter language model created by the maintainer elinas. It is a LoRA (Low-Rank Adaptation) trained model that has been quantized to 4-bit precision using the GPTQ method. This allows the model to be smaller in size and require less VRAM for inference, while maintaining reasonable performance. The maintainer provides several different versions of the quantized model, including ones with different group sizes to balance model accuracy and memory usage. This model is based on the larger llama-30b model, which was originally created by Meta. The LoRA fine-tuning was done by the team at Baseten. The maintainer elinas has further optimized the model through quantization and provided multiple versions for different hardware requirements. Model inputs and outputs Inputs Text**: The model takes text inputs, which can be prompts, instructions, or conversations. It is designed to be used in a conversational setting. Outputs Text**: The model generates relevant text responses based on the input. It can be used for tasks like question answering, text generation, and dialogue. Capabilities The alpaca-30b-lora-int4 model is a capable language model that can handle a variety of text-based tasks. It performs well on common benchmarks like C4, PTB, and Wikitext2. The quantized versions of the model allow for more efficient inference on hardware with limited VRAM, while still maintaining good performance. What can I use it for? This model can be useful for a wide range of natural language processing projects, such as building chatbots, virtual assistants, or content generation tools. The smaller quantized versions may be particularly helpful for deploying language models on edge devices or in resource-constrained environments. Things to try One key feature of this model is the ability to run it in a deterministic mode by turning off sampling. This can be helpful for applications that require consistent outputs. Additionally, the maintainer recommends using an instruction-based prompting format for best results, which can help the model follow the desired task more effectively.
Updated 5/28/2024
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llama-7b-hf-transformers-4.29
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The llama-7b-hf-transformers-4.29 is an open-source large language model developed by the FAIR team of Meta AI. It is a 7-billion parameter model based on the transformer architecture, and is part of the larger LLaMA family of models that also includes 13B, 33B, and 65B parameter versions. The model was trained between December 2022 and February 2023 on a mix of publicly available online data, including data from sources like CCNet, C4, GitHub, Wikipedia, Books, ArXiv, and Stack Exchange. The llama-7b-hf-transformers-4.29 model was converted to work with the latest Transformers library on Hugging Face, resolving some issues with the EOS token. It is licensed under a non-commercial bespoke license, and can be used for research on large language models, including exploring potential applications, understanding model capabilities and limitations, and developing techniques to improve them. Model inputs and outputs Inputs Text prompts of arbitrary length Outputs Continuation of the input text, generating coherent and contextually relevant language Capabilities The llama-7b-hf-transformers-4.29 model exhibits strong performance on a variety of natural language understanding and generation tasks, including commonsense reasoning, reading comprehension, and question answering. It was evaluated on benchmarks like BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, and others, demonstrating capabilities comparable to or better than other large language models like GPT-J. The model also shows promising results in terms of mitigating biases, with lower average bias scores across categories like gender, religion, race, and sexual orientation compared to the original LLaMA models. However, as with any large language model, the llama-7b-hf-transformers-4.29 may still exhibit biases and generate inaccurate or unsafe content, so it should be used with appropriate caution and safeguards. What can I use it for? The primary intended use of the llama-7b-hf-transformers-4.29 model is for research on large language models, such as exploring potential applications, understanding model capabilities and limitations, and developing techniques to improve them. Researchers in natural language processing, machine learning, and artificial intelligence would be the main target users for this model. While the model is not recommended for direct deployment in production applications without further risk evaluation and mitigation, it could potentially be used as a starting point for fine-tuning on specific tasks or domains, or as a general-purpose language model for prototyping and experimentation. Things to try One interesting aspect of the llama-7b-hf-transformers-4.29 model is its performance on commonsense reasoning tasks, which can provide insights into the model's understanding of the world and its ability to make inferences. Prompting the model with questions that require commonsense knowledge, such as "What is the largest animal?" or "What do you need to do to make a cake?", and analyzing its responses could be a fruitful area of exploration. Additionally, given the model's potential biases, it could be worthwhile to investigate the model's behavior on prompts related to sensitive topics, such as gender, race, or religion, and to develop techniques for mitigating these biases.
Updated 5/28/2024
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vicuna-13b-4bit
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The vicuna-13b-4bit model is a compressed version of the Vicuna 13B model, optimized for performance using the GPTQ 4-bit quantization technique. Vicuna is a high-coherence language model based on the LLaMA architecture, comparable to ChatGPT in capability. The model was created by elinas at Hugging Face. Similar models include the llama-7b-hf-transformers-4.29 and alpaca-30b-lora-int4 models, which are also based on the LLaMA architecture and optimized for performance using quantization techniques. Model inputs and outputs Inputs Prompt**: A text prompt that the model will use to generate a response. Outputs Generated text**: The model will generate a response based on the input prompt. The response will be coherent and relevant to the prompt. Capabilities The vicuna-13b-4bit model is capable of engaging in open-ended dialogue, answering questions, and generating human-like text on a variety of topics. It has been trained on a large corpus of text data and can draw upon this knowledge to provide informative and engaging responses. What can I use it for? The vicuna-13b-4bit model can be used for a variety of applications, such as building chatbots, generating creative writing, and answering questions. The model's compressed size and optimized performance make it well-suited for deployment on resource-constrained devices or in scenarios where real-time response is important. Things to try One interesting thing to try with the vicuna-13b-4bit model is to provide it with prompts that require reasoning or logical thinking. For example, you could ask the model to solve a math problem or provide an analysis of a complex topic. The model's strong performance on benchmarks like MMLU suggests that it may be capable of more advanced reasoning tasks. Another interesting avenue to explore is using the model in a collaborative setting, where users can engage in back-and-forth conversations and build upon each other's ideas. The model's ability to maintain coherence and context over multiple exchanges could make it a valuable tool for brainstorming or ideation.
Updated 9/6/2024
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alpaca-13b-lora-int4
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The alpaca-13b-lora-int4 model is a 13 billion parameter language model that has been trained using the LLaMA architecture and fine-tuned with the Alpaca dataset. This model has been further quantized to 4-bit precision using the GPTQ method, reducing the model size while maintaining performance. Compared to similar models like the alpaca-30b-lora-int4 and vicuna-13b-4bit, the alpaca-13b-lora-int4 model is a more compact version optimized for faster inference on lower-end hardware. Model inputs and outputs The alpaca-13b-lora-int4 model is a text-to-text transformer model, meaning it takes text as input and generates text as output. The model can be used for a variety of natural language processing tasks, including language generation, question answering, and summarization. Inputs Text prompts**: The model expects text prompts as input, which can be in the form of instructions, questions, or partial sentences. Outputs Generated text**: The model will generate coherent and contextually relevant text in response to the input prompt. Capabilities The alpaca-13b-lora-int4 model has been trained on a wide range of text data, giving it broad language understanding and generation capabilities. It can be used for tasks like answering questions, generating creative writing, and providing informative summaries. The model's 4-bit quantization also makes it efficient to run on resource-constrained hardware, making it a good choice for real-world applications. What can I use it for? The alpaca-13b-lora-int4 model can be used for a variety of natural language processing tasks, such as: Chatbots and virtual assistants**: The model can be used to build conversational AI systems that can engage in natural dialogue and assist users with a variety of tasks. Content generation**: The model can be used to generate text for applications like news articles, blog posts, or creative writing. Question answering**: The model can be used to answer questions on a wide range of topics, making it useful for educational or research applications. Things to try One interesting thing to try with the alpaca-13b-lora-int4 model is to experiment with different prompt formats and styles. For example, you could try providing the model with open-ended prompts, specific instructions, or even persona-based prompts to see how it generates different types of responses. Additionally, you could explore the model's performance on specialized tasks by fine-tuning it on domain-specific datasets.
Updated 9/6/2024