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English(EN) Does anyone have real experience with Ornith-1.5-9B for coding

用户寻求 Ornith-1.5-9B 模型实际编码性能数据

r/LocalLLaMA subreddit 上的一位用户正在寻求有关 Ornith-1.5-9B 编码模型的实际使用经验。他们发现 Qwen-3.8-27B 模型在他们的专业编码任务中表现出色,在专用的 RTX 3090 上运行,并取得了令人印象深刻的性能指标。虽然他们已经在一些简单的编码任务上测试过 Ornith-1.5-9B 并取得了一些成功,但他们希望获得官方基准测试之外的更多用户反馈。 AI

影响 这次讨论强调了在官方基准测试之外,对专业编码模型进行实际用户反馈的持续需求。

排序理由 用户讨论寻求特定模型的实际性能数据。

在 r/LocalLLaMA 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

用户寻求 Ornith-1.5-9B 模型实际编码性能数据

本文如何被排名

Signal score
3 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Commentary
用户讨论寻求特定模型的实际性能数据。
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, product
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
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准

报道来源 [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Barni275 ·

    有人用过 Ornith-1.5-9B 进行编码的实际经验吗

    <!-- SC_OFF --><div class="md"><p>I'm very happy with Qwen-3.8-27B, I run it on my work machine and switched for major part of real coding tasks from cloud subscriptions to it. I use one dedicated headless RTX 3090, and get maybe 1000-1500 tps prefill, and 45-60 tps generate, wit…