Llama-2-7b-chat-hf_1bitgs8_hqq
Maintainer: mobiuslabsgmbh
73
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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 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.
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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