axolotl/examples/glm45

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

1. Install Axolotl following the installation guide.

2. Install Cut Cross Entropy to reduce training VRAM usage.

3. 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.

Optimization Guides

Please check the Optimizations doc.

Related Resources

• GLM-4.5-Air on HuggingFace
• GLM-4.5 Blog
• Axolotl Docs
• Axolotl Website
• Axolotl GitHub
• Axolotl Discord