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Make llamacpp_HF 6x faster
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@ -56,7 +56,7 @@ class LlamacppHF(PreTrainedModel):
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else:
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self.model.eval([seq[-1]])
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logits = torch.tensor(self.model.eval_logits[-1]).view(1, 1, -1).to(kwargs['input_ids'].device)
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logits = torch.tensor(self.model.scores[self.model.n_tokens-1, :]).view(1, 1, -1).to(kwargs['input_ids'].device)
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else:
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self.model.reset()
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self.model.eval(seq)
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@ -254,7 +254,7 @@ def create_model_menus():
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shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa is currently 2x faster than AutoGPTQ on some systems. It is installed by default with the one-click installers. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
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shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
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shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s a bit slower than the regular ExLlama.')
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shared.gradio['llamacpp_HF_info'] = gr.Markdown('llamacpp_HF is a wrapper that lets you use llama.cpp like a Transformers model, which means it can use the Transformers samplers. It works, but it\'s experimental and slow. Contributions are welcome.\n\nTo use it, make sure to first download oobabooga/llama-tokenizer under "Download custom model or LoRA".')
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shared.gradio['llamacpp_HF_info'] = gr.Markdown('llamacpp_HF is a wrapper that lets you use llama.cpp like a Transformers model, which means it can use the Transformers samplers. To use it, make sure to first download oobabooga/llama-tokenizer under "Download custom model or LoRA".')
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with gr.Column():
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with gr.Row():
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