Mobiuslabsgmbh
Models by this creator
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Llama-2-7b-chat-hf_1bitgs8_hqq
73
The Llama-2-7b-chat-hf_1bitgs8_hqq model is an experimental 1-bit quantized version of the Llama2-7B-chat model, using a low-rank adapter to improve performance. Quantizing small models at such extreme low-bits is a challenging task, and the purpose of this model is to show the community what to expect when fine-tuning such models. The HQQ+ approach, which uses a 1-bit matmul with a low-rank adapter, helps the 1-bit base model outperform the 2-bit Quip# model after fine-tuning on a small dataset. Model inputs and outputs Inputs Text prompts Outputs Generative text responses Capabilities The Llama-2-7b-chat-hf_1bitgs8_hqq model is capable of producing human-like text responses to prompts, with performance that approaches more resource-intensive models like ChatGPT and PaLM. Despite being heavily quantized to just 1-bit weights, the model can still achieve strong results on benchmarks like MMLU, ARC, HellaSwag, and TruthfulQA, when fine-tuned on relevant datasets. What can I use it for? The Llama-2-7b-chat-hf_1bitgs8_hqq model can be used for a variety of natural language generation tasks, such as chatbots, question-answering systems, and content creation. Its small size and efficient quantization make it well-suited for deployment on edge devices or in resource-constrained environments. Developers could integrate this model into applications that require a helpful, honest, and safe AI assistant. Things to try Experiment with fine-tuning the Llama-2-7b-chat-hf_1bitgs8_hqq model on datasets relevant to your use case. The maintainers provide example datasets used for the chat model, including timdettmers/openassistant-guanaco, microsoft/orca-math-word-problems-200k, and meta-math/MetaMathQA. Try evaluating the model's performance on different benchmarks to see how the 1-bit quantization affects its capabilities.
Updated 5/28/2024
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Llama-3.1-8b-instruct_4bitgs64_hqq_calib
53
The Llama-3.1-8b-instruct_4bitgs64_hqq_calib model is an AI model maintained by mobiuslabsgmbh that is a quantized version of the Meta-Llama-3.1-8B-Instruct model. It uses the HQQ quantization technique to reduce the model size to 4-bits with a group size of 64, resulting in significantly reduced memory usage compared to the original FP16 model. This model is available in both calibrated and uncalibrated versions. Model inputs and outputs Inputs This model takes in text as input, which can be used for a variety of language tasks such as open-ended conversation, question answering, and code generation. Outputs The model generates text outputs, which can be used for tasks like text completion, summarization, and response generation. Capabilities The Llama-3.1-8b-instruct_4bitgs64_hqq_calib model is a highly capable language model that can be used for a variety of natural language processing tasks. It demonstrates strong performance on common benchmarks like ARC, HellaSwag, MMLU, TruthfulQA, and Winogrande, often outperforming other 4-bit quantized versions of the Llama 3.1 model. What can I use it for? This quantized Llama model can be useful for developers and researchers who need to deploy a powerful language model on resource-constrained devices or systems. The reduced memory footprint allows for faster inference times and lower hardware requirements, making it well-suited for applications like chatbots, virtual assistants, and code generation tools. Additionally, the calibrated version may be preferred for use cases that require more reliable and consistent outputs. Things to try One interesting aspect of this model is the ability to trade off memory usage and inference speed against output quality by selecting different quantization configurations. Developers can experiment with the HQQ 4-bit/gs-64 and AWQ 4-bit versions to find the optimal balance for their specific use case and hardware constraints.
Updated 9/6/2024