一位 Mastodon 用户分享了他们使用 mlx-community/Ornith-1.0-35B-OptiQ-4bit 模型的体验，并称赞其速度非凡。该帖子还提到了使用 Rapid-MLX 和混合缓存配置。 AI
影响 强调了在特定硬件/软件配置下，LLM 推理的潜在性能提升。
排序理由 用户对特定模型和工具的体验。
在 Mastodon — fosstodon.org 阅读 →
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mlx-community/Ornith-1.0-35B-OptiQ-4bit 模型因速度快而受到称赞
本文如何被排名
- Signal score
- 0 / 100
- Composite score across the factors below. Higher = stronger signal that this story matters right now.
- Newsworthiness bucket
- Tool
- 用户对特定模型和工具的体验。
- Source corroboration
- Single-source cluster
- Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
- Topics
- model release
- Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
- AI-industry relevance
- High
- Clearly on-topic for AI-industry coverage.
- Story freshness
- 88 days old
- Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.
完整方法见我们的编辑标准。
报道来源 [1]
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这速度太快了，这是什么... mlx-community/Ornith-1.0-35B-OptiQ-4bit Rapid-MLX --hybrid-cache-entries 6 # AI # LLM
むっちゃ速いんだけど何これ… mlx-community/Ornith-1.0-35B-OptiQ-4bit Rapid-MLX --hybrid-cache-entries 6 # AI # LLM