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Quarto GHA Workflow Runner
2026-03-17 02:47:04 +00:00
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@@ -809,7 +809,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
<ul>
<li>2026/03:
<ul>
<li>New model support has been added in Axolotl for <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3.5">Qwen3.5, Qwen3.5 MoE</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash">GLM-4.7-Flash</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v">GLM-4.6V</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45">GLM-4.5-Air</a>.</li>
<li>New model support has been added in Axolotl for [<a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/qwen3.5">Qwen3.5, Qwen3.5 MoE</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm47-flash">GLM-4.7-Flash</a>, <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm46v">GLM-4.6V</a>, and <a href="https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/glm45">GLM-4.5-Air</a>.</li>
<li><a href="https://docs.axolotl.ai/docs/expert_quantization.html">MoE expert quantization</a> support (via <code>quantize_moe_experts: true</code>) greatly reduces VRAM when training MoE models (FSDP2 compat).</li>
</ul></li>
<li>2026/02: