# Kimi Linear

Kimi Linear is a MoE model (48B total, 3B active) by MoonshotAI using a hybrid linear attention architecture to achieve a 1M token context length. It uses Kimi Delta Attention (KDA), a refined version of Gated DeltaNet that reduces KV cache size by up to 75% and boosts decoding throughput by up to 6x for long contexts.

This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.

Note: Axolotl uses experimental training code for Kimi Linear as their original modeling code is inference-only.

## Getting started

1. Install Axolotl following the installation guide.

2. Install CCE via docs

3. Run the finetuning example:

       axolotl train examples/kimi-linear/kimi-48b-lora.yaml

This config uses about 98.7GiB VRAM.

Let us know how it goes. Happy finetuning!

### TIPS

- Kimi Linear requires `trust_remote_code: true`.
- You can run a full finetuning by removing the `adapter: lora` and `load_in_8bit: true`.
- Read more on how to load your own dataset at docs
- The dataset format follows the OpenAI Messages format as seen here

## Optimization Guides

See 👉 docs.

## Limitations

This is not yet compatible with MoE kernels from transformers v5.

## Related Resources

- Kimi Linear Paper
- Kimi Linear GitHub
- Axolotl Docs
- Axolotl Website
- Axolotl GitHub
- Axolotl Discord