e4032fc90f
* upgrade to torchao 0.17.0 * chore: lint * refactor attention handling * replace legacy attention boolean flags with capability properties Replace checks with capability-based properties derived from attn_implementation This separates three concerns that were conflated under flash_attention: 1. Backend selection -> attn_implementation enum 2. Packing capability -> attn_supports_packing property 3. Flash-attn library dependency -> attn_uses_flash_lib property * compute attn capability flags in normalizer instead of properties * make attn_implementation the single source of truth * move attention-dependent validators to mode=after * migrate remaining consumers to canonical attn_implementation * expand attention tests + rewrite docs * migrate example configs to canonical attn_implementation * update doc snippets + reject gemma4-hybrid with non-FA2 backend * remove dead gemma4 branch in _set_attention_config * fix duplicate attn_implementation in gpt-oss yamls and flaky caplog tests * drop "Phase 2" naming from attn-implementation tests * regroup attn_implementation tests by feature concern * clean up verbose comments and remove MD Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai> * fix(collator): pass return_dict=True at apply_chat_template top level for transformers 5.x In transformers 5.x, ProcessorMixin.apply_chat_template gained its own `return_dict` parameter (defaulting to False). When return_dict=False and tokenize=True the method returns out["input_ids"] directly — a 2-D tensor — rather than the full BatchFeature dict. The old code placed `return_dict=True` inside processor_kwargs. In transformers 5.x those kwargs are forwarded to the underlying processor call self(...) where _merge_kwargs silently ignores any key not present in MllamaProcessorKwargs (emitting a warning). The outer return_dict therefore stayed False, apply_chat_template returned the raw input_ids tensor, and the subsequent `batch["input_ids"]` attempted to index a 2-D tensor with the 9-character string "input_ids", producing: IndexError: too many indices for tensor of dimension 2 The fix is to pass return_dict=True as a top-level keyword argument to apply_chat_template (where it is actually consumed) and remove it from processor_kwargs (where it was silently dropped). No version guard is needed: transformers is pinned to ==5.5.4 in pyproject.toml. Adds a unit-level regression test (tests/test_mm_chat_collator.py) that mocks the processor to return a raw tensor when apply_chat_template is called without top-level return_dict=True, verifying the four invariants: process_rows returns a dict, input_ids is 2-D, labels is 2-D, and apply_chat_template receives return_dict=True as a top-level kwarg. Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_multimodal_dataset Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_text_only_dataset Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai> * fix(collator): process_rows returns dict (BatchFeature) shape Two related changes for the multimodal chat collator under transformers 5.x: 1. Wrap apply_chat_template result in dict(...) so process_rows returns a plain dict rather than a BatchFeature instance. BatchFeature is a Mapping but not a dict; downstream code that did batch["labels"] = self.processing_strategy.process_labels(batch["input_ids"]) would index on a tensor when the result wasn't dict-shaped, raising IndexError: too many indices for tensor of dimension 2 2. Soften the regression test's contract from `dict` to `Mapping` so it exercises the actual semantic guarantee (key/value access) rather than the implementation detail (dict vs BatchFeature). Test guards against the original transformers 5.x breakage where apply_chat_template's return_dict default went from True to False. Includes regression test under tests/test_mm_chat_collator.py. Bug surfaced via swarm dispatch task_01KQHPNAYD8XARSNSDJVW1GPF6 against attn-implementation-refactor; squash-merged from agent commits 4de886fd + dc9fcf4f. Signed-off-by: Wing Lian <wing@axolotl.ai> --------- Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>
Finetune Z.ai's GLM-4.5-Air with Axolotl
GLM-4.5-Air is a MoE model by Z.ai.
This guide shows how to fine-tune it with Axolotl.
Getting started
-
Install Axolotl following the installation guide.
-
Install Cut Cross Entropy to reduce training VRAM usage.
-
Run the finetuning example:
# QLoRA (1x80GB @ ~63.4GiB/GPU)
axolotl train examples/glm45/glm-45-air-qlora.yaml
Dataset
In addition to the standard OpenAI Messages format, GLM-4.5 supports an extra parameter for thinking in the assistant section.
{
"role": "assistant",
"reasoning_content": "...", // or have </think>...</think> in `content`
"content": "..."
}
Make sure you set the below extra attributes if needed:
datasets:
- path: ...
type: chat_template
message_property_mappings:
role: role
content: content
# tool_calls: tool_calls # uncomment if using tools
# reasoning_content: reasoning_content # uncomment if have reasoning
# Uncomment if training on tool role (you would rarely if ever need this)
# eot_tokens:
# - <|observation|>
Tips
- The role name for tools in this template is
tool. - You will see this Axolotl WARNING — this is expected as the template does not use EOS:
EOS token '<|endoftext|>' not found in chat_template. Please check if your template/EOS token is correct. - You can run a full finetuning by removing
adapter: qlora,load_in_4bit: true, andquantize_moe_experts: truefrom the config. - LoRA kernels: Incompatible with this model. Must be explicitly disabled (
lora_*_kernel: false). - Read more on how to load your own dataset at docs.
GGUF / llama.cpp loading error (missing tensors)
If you see missing tensor 'blk.X.attn_norm.weight' when loading a GLM-4 / GLM4-MoE model in llama.cpp, this is likely
caused by num_nextn_predict_layers being set to 1 in config.json while the MTP weights were not exported (possible
after PEFT/QLoRA training).
Fix: Set "num_nextn_predict_layers": 0 in your config.json before converting to GGUF.
Optimization Guides
Please check the Optimizations doc.