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Four Qwen3.8-27B fine-tunes and merges on a coding benchmark: none beat the base
ArticleAI CodingAtlassian Forge

Four Qwen3.8-27B fine-tunes and merges on a coding benchmark: none beat the base

We ran four popular Qwen3.8-27B fine-tunes and merges through Forge 2.0, our long agentic coding benchmark, on one 64 GB MacBook next to the stock base, one build each in our own 8-bit conversions. In our setup one model's write calls came back malformed, one wrote careful code and then got stuck hunting for a method that doesn't exist, one finished fast with placeholders, and the merge got the hardest parts right and scored 0.1105. The base's median is 0.1852.

Mihai PerdumOct 19, 202617 min read

More articles

v0.5 of our Atlassian Qwen3.8-27B: beats v0.4, now with GGUF for llama.cpp and Ollama
ArticleLocal AI

v0.5 of our Atlassian Qwen3.8-27B: beats v0.4, now with GGUF for llama.cpp and Ollama

v0.5 of the Qwen3.8-27B we fine-tune for Forge, Jira, Confluence and JSM is out. It beat v0.4 on every gate we measure, with no waiver needed this time, using a training method that fixed the one thing capacity kept breaking. GGUF versions for llama.cpp and Ollama, Q8_0 and Q6_K, are now published alongside the existing MLX release, parity proven across three separate measured links plus a served-model probe.

Mihai PerdumSep 20, 202611 min read

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