QAT and quantization w/torchao
This commit is contained in:
salman
2025-05-28 12:35:47 +01:00
committed by GitHub
parent 20fda75917
commit 5fca214108
26 changed files with 1372 additions and 13 deletions

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base_model: meta-llama/Llama-3.2-3B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
load_in_8bit: false
load_in_4bit: false
strict: false
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
datasets:
- path: yahma/alpaca-cleaned
type: alpaca
output_dir: ./outputs/qat_out/
sample_packing: true
pad_to_sequence_len: true
sequence_len: 512
flex_attention: true
flex_attn_compile_kwargs:
dynamic: false
mode: max-autotune-no-cudagraphs
qat:
activation_dtype: int8
weight_dtype: int4
group_size: 32
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 1
optimizer: adamw_torch_fused
cosine_constant_lr_ratio: 0
cosine_min_lr_ratio: 1.0
learning_rate: 2e-5
save_only_model: true
bf16: true
resume_from_checkpoint:
logging_steps: 1
evals_per_epoch: 1
saves_per_epoch: 1
warmup_steps: 10
weight_decay: 0.0
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_version: 2
fsdp_offload_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
fsdp_reshard_after_forward: true
fsdp_activation_checkpointing: true
special_tokens:
pad_token: <|end_of_text|>

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base_model: Qwen/Qwen3-8B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
load_in_8bit: false
load_in_4bit: false
strict: false
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
datasets:
- path: tatsu-lab/alpaca
type: alpaca
output_dir: ./outputs/qat_out/
sequence_len: 2048
sample_packing: true
flex_attention: true
pad_to_sequence_len: true
flex_attn_compile_kwargs:
dynamic: false
mode: max-autotune-no-cudagraphs
qat:
activation_dtype: int8
weight_dtype: int4
group_size: 256
fake_quant_after_n_steps: 1000
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 2
max_steps: 2000
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5
bf16: true
tf32: true
resume_from_checkpoint:
logging_steps: 1
evals_per_epoch: 1
saves_per_epoch: 1
warmup_steps: 10
weight_decay: 0.0
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_version: 2
fsdp_offload_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
fsdp_reshard_after_forward: true
fsdp_activation_checkpointing: true
special_tokens: