AI is happening. The future is updating.Sharp-MiniCPM5-2B-GGUF
Dynamic **imatrix** quants of openbmb/MiniCPM5-2B, a 2.5B dense 131k-context model, carrying our cyber-and-coding-weighted importance matrix, an unsloth-dynamic-style per-tensor quant policy, and the **Sharp-MiniCPM** chat template with fixes and an improved system prompt. We recommend the Q6_K_XL for anyone who can run it. Practically lossless, at under half the size of the F16 model, and scoring higher than stock Q8_0 on SWE-bench-Live. **99% KL** is the 99th percentile of KL divergence against the BF16 weights on held-out code — the rare token where a quant actually diverges. **top-1** is how often the quant picks the same next token as BF16. The last three columns are the largest `q8_0` KV context that fits beside the weights on a 4 / 6 / 8 GB card.
Dynamic **imatrix** quants of openbmb/MiniCPM5-2B, a 2.5B dense 131k-context model, carrying our cyber-and-coding-weighted importance matrix, an unsloth-dynamic-style per-tensor quant policy, and the **Sharp-MiniCPM** chat template with fixes and an improved system prompt. We recommend the Q6_K_XL for anyone who can run it. Practically lossless, at under half the size of the F16 model, and scoring higher than stock Q8_0 on SWE-bench-Live. **99% KL** is the 99th percentile of KL divergence against the BF16 weights on held-out code — the rare token where a quant actually diverges. **top-1** is how often the quant picks the same next token as BF16. The last three columns are the largest `q8_0` KV context that fits beside the weights on a 4 / 6 / 8 GB card.