AI正在发生 未来正在更新DeepSeek-R1-0528-Qwen3-8B-heretic-GGUF
GGUF quantizations of richardyoung/DeepSeek-R1-0528-Qwen3-8B-heretic, an abliterated build of deepseek-ai/DeepSeek-R1-0528-Qwen3-8B (DeepSeek's R1-0528 reasoning distilled into Qwen3-8B: a reasoning model that emits chains before answering) made with Heretic. The very low KL divergence (0.041, far below the 0.5 “damage” threshold) means the model retains essentially all of its original capabilities and coherence. Each quant compared with the F16 model using `llama-perplexity --kl-divergence` on the wikitext-2 test set (40 × 512-token chunks). KL divergence measures how far the quant's next-token probabilities drift from F16; lower is better. Top-token match is how often the quant picks the same next token as F16. As a rough guide, a KLD under 0.01 is hard to tell from F16, and from about 0.05 differences start to show in longer outputs.
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GGUF quantizations of richardyoung/DeepSeek-R1-0528-Qwen3-8B-heretic, an abliterated build of deepseek-ai/DeepSeek-R1-0528-Qwen3-8B (DeepSeek's R1-0528 reasoning distilled into Qwen3-8B: a reasoning model that emits chains before answering) made with Heretic. The very low KL divergence (0.041, far below the 0.5 “damage” threshold) means the model retains essentially all of its original capabilities and coherence. Each quant compared with the F16 model using `llama-perplexity --kl-divergence` on the wikitext-2 test set (40 × 512-token chunks). KL divergence measures how far the quant's next-token probabilities drift from F16; lower is better. Top-token match is how often the quant picks the same next token as F16. As a rough guide, a KLD under 0.01 is hard to tell from F16, and from about 0.05 differences start to show in longer outputs.