Files
axolotl/examples/kimi-linear/kimi-48b-lora.yaml
NanoCode012 97f1b1758d Feat: add kimi linear support (#3257)
* feat: add custom kimi linear patch [skip ci]

* feat: add configuration file and fix import [skip ci]

* fix: hijack tokenizer temporarily [skip ci]

* chore: remove accidental commit

* fix: attempt patch kimi remote

* fix: kwargs passsed

* fix: device for tensor

* fix: aux loss calculation

* feat: cleaned up patches order

* fix: remove duplicate tokenizer patch

* chore: add debug logs

* chore: add debug logs

* chore: debug

* Revert "chore: add debug logs"

This reverts commit da372a5f67.

* Revert "chore: add debug logs"

This reverts commit 97d1de1d7c.

* fix: KeyError: 'tokenization_kimi'

* fix: support remote_model_id in cce patch

* feat: add config preload patch

* fix: use standard aux loss calc and updated modeling

* fix: import

* feat: add kimi-linear docs and example

* chore: add note about moe kernels

* feat: update cce to include kimi-linear

* chore: lint

* chore: update main readme

* fix: patch mechanism to address comments

* chore: lint

* fix: tests

* chore: cleanup comment
2025-12-25 17:53:52 +07:00

82 lines
1.3 KiB
YAML

base_model: moonshotai/Kimi-Linear-48B-A3B-Instruct
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
trust_remote_code: true
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: fozziethebeat/alpaca_messages_2k_test
type: chat_template
split: train
dataset_prepared_path: last_run_prepared
val_set_size: 0.2
output_dir: ./outputs/lora-out
adapter: lora
lora_model_dir:
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_ratio: 0.1
evals_per_epoch: 2
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens: