See axolotl config
axolotl version: 0.13.0.dev0
# !pip install transformers==4.55.4
# !pip install --no-deps trl==0.22.2
# !pip install --no-build-isolation mamba_ssm==2.2.5
# !pip install --no-build-isolation causal_conv1d==1.5.2
# === Model Configuration ===
base_model: apertus/trained-instruct-attn
load_in_8bit: false
load_in_4bit: false
# === HF Configuration ===
#hub_model_id: ToastyPigeon/muse-marvin-32k-lora-2
#hub_strategy: "every_save"
output_dir: apertus/trained-again-instruct-o-down
# === Wandb Tracking ===
wandb_project: ApertusTests
# wandb_entity: [WANDB_ENTITY]
wandb_name: trained-again-instruct-o-down
# === Training Setup ===
num_epochs: 1
micro_batch_size: 1
gradient_accumulation_steps: 4
sequence_len: 4096
#sequence_parallel_degree: 2
#heads_k_stride: 1
sample_packing: true
#pad_to_sequence_len: true
#temperature: 0.7
#max_steps: 10
# === Evaluation ===
val_set_size: 0.025
evals_per_epoch: 10
#eval_steps: 20
#max_steps: 60
#eval_table_size:
eval_max_new_tokens: 128
#eval_sample_packing: true
#eval_strategy: "no"
# === LoRA Configuration ===
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_target_modules:
# - up_proj
# - down_proj
# - gate_proj
# - q_proj
# - v_proj
# - k_proj
# - o_proj
# - input_layernorm
# - post_attention_layernorm
# - embed_tokens
# - lm_head
lora_fan_in_fan_out:
#peft_use_rslora: true
lora_modules_to_save:
# - embed_tokens
# - lm_head
#fix_untrained_tokens: true
#lora_mlp_kernel: true
#lora_qkv_kernel: true
#lora_o_kernel: true
unfrozen_parameters:
- model.layers.[0-9]+.self_attn.o_proj
- model.layers.[0-9]+.mlp.down_proj
# === Hyperparameter Configuration ===
#optimizer: apollo_adamw_layerwise
#warmup_steps: 0
warmup_ratio: 0.025
optimizer: adamw_torch_fused
#optimizer: paged_adamw_8bit
#optim_args:
# enable_stochastic_rounding: true
# enable_cautious: true
# enable_8bit: true
# Apollo-mini configuration:
#optim_args: "proj=random,rank=128,scale=128.0,scale_type=tensor,update_proj_gap=100"
# Regular Apollo configuration:
# optim_args:
#optim_target_modules: all_linear
learning_rate: 1e-5
lr_scheduler: cosine
#cosine_min_lr_ratio: 0.2
#lr_scheduler: cosine_with_min_lr
#lr_scheduler_kwargs:
# cosine_min_lr: 1e-6
weight_decay: 0.01
max_grad_norm: 1.0
#warmup_steps: 0
#warmup_ratio: 0.025
# === Data Configuration ===
#
#chat_template: jinja
chat_template: chatml
special_tokens:
# eos_token: "<|im_end|>"
# eos_token: "</s>"
#tokenizer_use_mistral_common: true
shuffle_merged_datasets: true
datasets:
# - path: grimulkan/LimaRP-augmented
# type: chat_template
# field_messages: conversations
# message_property_mappings:
# role: from
# content: value
# - path: allenai/tulu-3-sft-personas-instruction-following
# type: chat_template
# split: train[:10%]
# - path: ToastyPigeon/mixed-medical-reasoning-formatted
# type: chat_template
# data_files: mixed-medical-thinking.json
# split: train[:10%]
# - path: ToastyPigeon/steve-and-marvin
# type: completion
# data_files: marvin.json
# - path: ToastyPigeon/kimi-stories-completion
# type: completion
# - path: ToastyPigeon/new-story-dataset
# type: customcompletion-regex
# type: completion
# data_files: new-story-dataset-v2.json
# - path: allura-org/fujin-instruct-v2
# type: customchatml-regex
# type: chat_template
# field_messages: conversations
# message_property_mappings:
# role: from
# content: value
# - path: ToastyPigeon/some-rp-extended
# type: customchatml-regex
# type: chat_template
# field_messages: conversations
# message_property_mappings:
# role: from
# content: value
# roles_to_train: ["user","assistant"]
- path: allura-forge/koto-instruct-sft
# type: customchatml-regex
type: chat_template
split: train[50%:]
field_messages: conversations
message_property_mappings:
role: from
content: value
# - path: ToastyPigeon/SpringDragon
# type: customcompletion-regex
# type: completion
# split: train
# - path: ToastyPigeon/some-erotica
# type: customcompletion-regex
# type: completion
# split: train[:10%]
# - path: ToastyPigeon/tulu-mini
# type: chat_template
dataset_prepared_path: last_run_prepared
# === Plugins ===
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
# === Hardware Optimization ===
#gradient_checkpointing: true
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
#liger_fused_linear_cross_entropy: true
cut_cross_entropy: true
#deepspeed: ../axolotl/deepspeed_configs/zero2.json
# === FSDP Config ===
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: true
fsdp_activation_checkpointing: true
fsdp_use_orig_params: true
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: ApertusDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
# === Checkpointing ===
#save_steps: 10
saves_per_epoch: 1
save_total_limit: 1
# === Advanced Settings ===
bf16: auto
flash_attention: true
train_on_inputs: false
group_by_length: false
save_safetensors: true
logging_steps: 1
gc_steps: 10
seed: 69
apertus/trained-again-instruct-o-down
This model was trained from scratch on the allura-forge/koto-instruct-sft dataset. It achieves the following results on the evaluation set:
- Loss: 0.9467
- Memory/max Active (gib): 5.15
- Memory/max Allocated (gib): 5.15
- Memory/device Reserved (gib): 6.41
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 69
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 2
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 12
- training_steps: 516
Training results
| Training Loss | Epoch | Step | Validation Loss | Active (gib) | Allocated (gib) | Reserved (gib) |
|---|---|---|---|---|---|---|
| No log | 0 | 0 | 1.0191 | 6.25 | 5.19 | 6.43 |
| 0.8751 | 0.1008 | 52 | 0.9954 | 5.15 | 5.15 | 6.41 |
| 1.0313 | 0.2016 | 104 | 0.9796 | 5.15 | 5.15 | 6.41 |
| 1.0144 | 0.3023 | 156 | 0.9677 | 5.15 | 5.15 | 6.41 |
| 1.0103 | 0.4031 | 208 | 0.9606 | 5.15 | 5.15 | 6.41 |
| 0.862 | 0.5039 | 260 | 0.9553 | 5.15 | 5.15 | 6.41 |
| 0.9892 | 0.6047 | 312 | 0.9512 | 5.15 | 5.15 | 6.41 |
| 1.0593 | 0.7054 | 364 | 0.9488 | 5.15 | 5.15 | 6.41 |
| 0.9527 | 0.8062 | 416 | 0.9474 | 5.15 | 5.15 | 6.41 |
| 0.8602 | 0.9070 | 468 | 0.9467 | 5.15 | 5.15 | 6.41 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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