---
library_name: peft
tags:
- axolotl
- base_model:adapter:apertus-12b-nonzero-trained/cpt-part2-instruct-part1
- lora
- transformers
datasets:
- grimulkan/LimaRP-augmented
- ToastyPigeon/mixed-medical-reasoning-formatted
- ToastyPigeon/kimi-stories-instruct
- allura-org/fujin-instruct-v2
- ToastyPigeon/some-rp-extended
- allura-forge/koto-instruct-sft-nothink
base_model: apertus-12b-nonzero-trained/cpt-part2-instruct-part1
pipeline_tag: text-generation
model-index:
- name: apertus-12b-nonzero-trained/part2-instruct
results: []
---
[
](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.13.0.dev0`
```yaml
# === Model Configuration ===
base_model: apertus-12b-nonzero-trained/cpt-part2-instruct-part1
load_in_8bit: false
load_in_4bit: false
# === HF Configuration ===
#hub_model_id: ToastyPigeon/apertus-12b-try-again-s1
#hub_strategy: "every_save"
output_dir: apertus-12b-nonzero-trained/part2-instruct
# === Wandb Tracking ===
wandb_project: ApertusV3
# wandb_entity: [WANDB_ENTITY]
wandb_name: 12b-part2-instruct
# === Training Setup ===
num_epochs: 1
micro_batch_size: 2
gradient_accumulation_steps: 16
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: 200
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
lora_model_dir:
lora_r: 128
lora_alpha: 16
lora_dropout: 0
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.(2[4-9]|3[0-9]).*
# - model.layers.[0-9+].mlp.up_proj
# - model.layers.[0-9]+.mlp.down_proj
# - model.layers.[0-9+].feedforward_layernorm
# - embed_tokens
# - lm_head
# - model.layers.[0-9]+.self_attn.(q|k|v|o)_proj
# === Hyperparameter Configuration ===
#optimizer: apollo_adamw_layerwise
#warmup_steps: 0
warmup_ratio: 0.025
#optimizer: adamw_8bit
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: 2e-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: 2.0
#warmup_steps: 0
#warmup_ratio: 0.025
# === Data Configuration ===
#
#chat_template: jinja
chat_template: chatml
special_tokens:
eos_token: "<|im_end|>"
# eos_token: ""
#tokenizer_use_mistral_common: true
shuffle_merged_datasets: true
datasets:
# - path: allura-org/the-anarchist-library
# type: completion
# split: train[:20%]
- 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-nothink.json
# split: train[:10%]
# - path: ToastyPigeon/steve-and-marvin
# type: completion
# data_files: marvin.json
- path: ToastyPigeon/kimi-stories-instruct
type: chat_template
# 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"]
split: train[:30%]
# - path: Alfitaria/rosier-inf
# type: completion
# split: train[70%:]
- path: allura-forge/koto-instruct-sft-nothink
# 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/erotic-books-clone
# type: customcompletion-regex
# type: completion
# split: train[:50%]
# split: train[35%:45%]
# - 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
#fsdp_stage: 2
#fsdp_final_state_dict_type: FULL_STATE_DICT
# === Checkpointing ===
#save_steps: 2
saves_per_epoch: 4
save_total_limit: 4
# === Advanced Settings ===
bf16: true
flash_attention: true
train_on_inputs: false
group_by_length: false
save_safetensors: true
logging_steps: 1
seed: 420
gc_steps: 10
```
# apertus-12b-nonzero-trained/part2-instruct
This model was trained from scratch on the grimulkan/LimaRP-augmented, the ToastyPigeon/mixed-medical-reasoning-formatted, the ToastyPigeon/kimi-stories-instruct, the allura-org/fujin-instruct-v2, the ToastyPigeon/some-rp-extended and the allura-forge/koto-instruct-sft-nothink datasets.
It achieves the following results on the evaluation set:
- Loss: 1.1911
- Memory/max Active (gib): 6.89
- Memory/max Allocated (gib): 6.88
- Memory/device Reserved (gib): 8.18
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 420
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 4
- 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: 9
- training_steps: 372
### Training results
| Training Loss | Epoch | Step | Validation Loss | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:------:|:----:|:---------------:|:------------:|:---------------:|:--------------:|
| No log | 0 | 0 | 1.4636 | 6.87 | 6.87 | 8.13 |
| 1.2677 | 0.1020 | 38 | 1.3280 | 6.89 | 6.88 | 8.18 |
| 1.1286 | 0.2041 | 76 | 1.2605 | 6.89 | 6.88 | 8.18 |
| 1.159 | 0.3061 | 114 | 1.2275 | 6.89 | 6.88 | 8.18 |
| 1.0281 | 0.4081 | 152 | 1.2122 | 6.89 | 6.88 | 8.18 |
| 1.0781 | 0.5102 | 190 | 1.2033 | 6.89 | 6.88 | 8.18 |
| 1.0296 | 0.6122 | 228 | 1.1976 | 6.89 | 6.88 | 8.18 |
| 1.0756 | 0.7142 | 266 | 1.1939 | 6.89 | 6.88 | 8.18 |
| 1.1134 | 0.8162 | 304 | 1.1921 | 6.89 | 6.88 | 8.18 |
| 1.0437 | 0.9183 | 342 | 1.1911 | 6.89 | 6.88 | 8.18 |
### Framework versions
- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1