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axolotl/examples/magistral/magistral-small-fsdp-qlora.yaml
NanoCode012 2974670bf8 Feat: add arcee (#3028)
* feat: add arcee

* feat: add latest models supported by cce

* feat: add arcee example config

* chore: lint

* fix: typo

* feat: change to instruct

* feat: add vram usage

* Update README.md
2025-08-08 08:09:11 -04:00

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YAML

base_model: mistralai/Magistral-Small-2506
# Enable to use mistral-common tokenizer
tokenizer_use_mistral_common: true
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
load_in_8bit: false
load_in_4bit: true
datasets:
- path: fozziethebeat/alpaca_messages_2k_test
type: chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/lora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
eval_sample_packing: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
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: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: false
gradient_checkpointing:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 1
saves_per_epoch: 1
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
fsdp_activation_checkpointing: true
# save_first_step: true # uncomment this to validate checkpoint saving works with your config