1bitllm
Models by this creator
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bitnet_b1_58-3B
170
The bitnet_b1_58-3B model is a large language model developed by the maintainer 1bitLLM and trained on the RedPajama dataset. It is a reproduction of the BitNet b1.58 paper, and follows the hyperparameters and training techniques suggested in their follow-up paper. This model is available open-source in the 1bitLLM repository on Hugging Face. The bitnet_b1_58-3B model is part of a series of 700M, 1.3B, and 3B parameter models that demonstrate the capabilities of 1-bit language models. These models exhibit strong performance on a range of language tasks, including perplexity, arithmetic, and other benchmarks, while using significantly less memory and computation compared to full-precision models. Model inputs and outputs Inputs Text prompts for natural language generation tasks Outputs Coherent, human-like text continuations based on the input prompt Capabilities The bitnet_b1_58-3B model has demonstrated strong performance on a variety of language tasks. It achieves a perplexity of 9.88 on the test set, which is comparable to the reported 9.91 for the 3B parameter BitNet model. The model also performs well on other tasks like arithmetic reasoning (ARC), common sense reasoning (HellaSwag), and multi-choice QA (MMLU), achieving competitive zero-shot accuracies. One of the key capabilities of this model is its ability to deliver strong performance while using highly quantized 1-bit weights. This makes the model more memory and compute efficient, potentially enabling deployment on resource-constrained devices. What can I use it for? The bitnet_b1_58-3B model can be used for a variety of natural language processing tasks, such as: Text generation**: The model can be used to generate coherent, human-like text continuations based on input prompts. This could be useful for applications like creative writing, dialog systems, and content generation. Question answering**: The model's strong performance on benchmarks like MMLU suggests it could be used for answering a wide range of questions, potentially across different domains. Arithmetic reasoning**: The model's ability to perform well on the ARC benchmark indicates it could be used for tasks involving numerical reasoning and problem-solving. Deployment on edge devices**: The highly quantized nature of the model's weights could make it suitable for deployment on resource-constrained devices, enabling on-device language processing capabilities. Things to try One interesting aspect of the bitnet_b1_58-3B model is its ability to achieve strong performance using 1-bit weights. This suggests that further research into highly quantized language models could lead to more memory and compute-efficient architectures, potentially enabling new applications and use cases. Researchers and developers interested in this model could explore fine-tuning it on specific tasks or datasets, as well as investigating techniques for further improving the efficiency of 1-bit language models.
Updated 5/28/2024
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bitnet_b1_58-large
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bitnet_b1_58-large is a reproduction of the BitNet b1.58 model, a large language model developed by 1bitLLM. The model was trained on the RedPajama dataset, a reproduction of the LLaMA training dataset, using the training techniques described in the BitNet paper. This includes a two-stage learning rate schedule and weight decay, which the maintainer claims improves model performance. Similar models include the bitnet_b1_58-3B, another BitNet b1.58 reproduction at a larger 3 billion parameter scale, as well as the OLMo-Bitnet-1B and OpenLLaMA models, which use similar 1-bit techniques but are trained on different datasets. Model inputs and outputs Inputs Text sequences of up to 2048 tokens Outputs Continuation of the input text, generating new tokens autoregressively Capabilities The bitnet_b1_58-large model exhibits strong text generation capabilities, as demonstrated by its low perplexity scores and high accuracy on a variety of language understanding benchmarks. It performs comparably to or better than the FP16 version of the original BitNet b1.58 model across tasks like ARC, BoolQ, and WGE. This suggests the 1-bit quantization techniques used in training do not significantly degrade the model's performance. What can I use it for? The bitnet_b1_58-large model could be used for a variety of natural language processing tasks, such as text generation, language modeling, and open-ended question answering. Its compact 1-bit representation also makes it potentially useful for deployment in resource-constrained environments. However, the model is still relatively new and its performance may be limited compared to larger, more extensively trained language models. Developers should carefully evaluate the model's capabilities on their specific use case before deploying it in production. Things to try Experimenters could explore fine-tuning the bitnet_b1_58-large model on domain-specific datasets to see if its performance can be further improved for particular applications. The model's efficient 1-bit representation could also be leveraged to run it on low-power devices or in edge computing scenarios. Additionally, comparing the model's performance to other similar 1-bit language models like OLMo-Bitnet-1B or OpenLLaMA could yield interesting insights about the trade-offs between model size, training data, and quantization techniques.
Updated 9/6/2024