One line of Python to extend your LLM's context window 10x
Your LLM is running out of memory at 128K tokens. Here is the fix. from nexusquant import nexusquant with nexusquant(model): output = model.generate(input_ids, max_new_tokens=500) That is the entir...
Source: DEV Community
Your LLM is running out of memory at 128K tokens. Here is the fix. from nexusquant import nexusquant with nexusquant(model): output = model.generate(input_ids, max_new_tokens=500) That is the entire change. Before: 128K tokens, 40 GB KV cache memory on Llama-3-70B. After: 1.3M tokens, same 40 GB. 10x context window. Zero retraining. The pipeline compresses KV cache in four stages — normalization, Hadamard rotation, E8 lattice quantization, temporal delta coding — at 7x compression with -2.26% perplexity on Mistral-7B. Training-free. Drop-in. One context manager. If you are building long-context applications and memory is your ceiling, this is worth ten minutes. GitHub: github.com/nexusquant/nexusquant Best regards, João Marques
