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The document says. Slow training: enable model.use_unsloth: true; check distributed.cpu_offload: false; 'Mixed-precision misconfigured (use bfloat16, not float16)'.
What the code does.model.use_unsloth and distributed.cpu_offload do not exist and fail validation (verified). Unsloth is model.backend: "unsloth". Offload is distributed.fsdp_offload or the DeepSpeed preset. Training mixed precision is not configurable: bf16 is chosen automatically when CUDA supports it, otherwise fp16 (forgelm/trainer.py:516-517).
User impact. Users following the advice get a config validation error (exit 1) or look for a precision key that does not exist.
Proposed correction. 1. Not using Unsloth (model.backend: "unsloth", Linux + CUDA). 3. CPU offload enabled (distributed.fsdp_offload: true or a ZeRO-3-offload DeepSpeed preset). 4. GPU without bf16 support — ForgeLM picks bf16 automatically when available, else fp16.
Turkish mirror. the Turkish page has the same error; fix both in the same PR
Acceptance criteria
Each statement is re-checked against the code at fix time.
Documentation listed above is corrected in English and in its Turkish mirror in the same PR.
Every finding in this issue is resolved or explicitly closed with a reason.
Found by the October 2026 ForgeLM code and documentation review against commit cab2947. Code links point at that commit; line numbers may have moved since. Finding IDs (CR-…, DOC-…, TECH-…) are stable references for this backlog.
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