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license: other |
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# Model Card for llama-30b-hf-53q_4bit-128g_WVU |
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## Model Description |
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`llama-30b-hf-53q_4bit-128g_WVU` is a model based on the |
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Llama architecture with 30 billion parameters. |
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This model adopts a quantization in which the first 53 layers |
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of the decoder have been quantized with the [`gptq`](https://github.com/qwopqwop200/GPTQ-for-LLaMa) method, |
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which uses 4-bit precision and 128 groups. |
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Then, the last 7 decoder layers (1/8 of decoding layers), and lm_head have been fine-tuned using the [wizard_vicuna_70k_unfiltered dataset](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered), 1 epoch. |
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## Note |
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Quantization effectively reduces memory usage, however, it may result in differences in the parameters. |
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Additionally, fine-tuning only the last few layers lowers memory requirements for training but could lead to minor performance degradation. |
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Several alternatives exist for fine-tuning and quantizing the Llama models. The specific method utilized here—quantizing several layers, |
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followed by fine-tuning the last few layers—is designed to account for errors introduced during quantization (which sometimes can result in unexpected answers), |
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and enables the last few layers to be fine-tuned considering both the quantization error and the dataset. |
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It is worth mentioning that other methods may yield superior performance. For instance: |
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1. Fine-tuning the entire model for `X` epochs |
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2. Quantizing the first `K` layers |
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3. Fine-tuning the remaining layers for `Y` epochs |
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Nonetheless, as fine-tuning the entire model requires considerable resources (for example, 4 GPUs with 80GB VRAM is required for 7B LLaMa), |
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this model omit the first step from the method described above, and it works. |
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## Using the Model |
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To load the model, a custom `LlamaForCausalLM` is required. |
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You can find quantized llama [here](https://github.com/LearnItAnyway/quantized_llama). |
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## References |
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1. Meta - LLaMA |
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2. [WizardLM](https://github.com/nlpxucan/WizardLM) |
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3. [GPTQ for LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa) |
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4. [Wizard Vicuna Unfiltered Dataset](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered) |
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5. Various unlisted but great works, researches, and projects. |
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