Files

Mario Župan 96ae8bdd1d Add troubleshooting note for GLM4 GGUF MTP mismatch (#3559 ) [skip ci]

* Add troubleshooting note for GLM4 GGUF MTP mismatch

* Fix JSON syntax for num_nextn_predict_layers example

* fix: concise

---------

Co-authored-by: NanoCode012 <nano@axolotl.ai>

2026-04-01 10:05:06 -04:00

2.7 KiB

Raw Blame History

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, and quantize_moe_experts: true from 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.

2.7 KiB Raw Blame History