library_name: peft
tags:
- axolotl
- base_model:adapter:nicoboss/MedraN-E4B
- lora
- transformers
datasets: - ICEPVP8977/Uncensored_Small_Reasoning
pipeline_tag: text-generation
base_model: nicoboss/MedraN-E4B
model-index: - name: MedraN-E4B-Uncensored-Lora
results: []
See axolotl config
axolotl version: 0.12.0.dev0
base_model: ./HDD/MedraN_final/merged
processor_type: AutoProcessor
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
cut_cross_entropy: true
# for use with fft to only train on language model layers
# unfrozen_parameters:
# - model.language_model.*
# - lm_head
# - embed_tokens
load_in_8bit: false
load_in_4bit: false
# these 3 lines are needed for now to handle vision chat templates w images
skip_prepare_dataset: true
remove_unused_columns: false
sample_packing: false
# gemma3 doesn't seem to play nice with ddp
ddp_find_unused_parameters: true
chat_template: gemma3n
eot_tokens:
- <end_of_turn>
datasets:
- path: /root/Uncensored_Reasoner_Small_Chat.json
type: chat_template
field_messages: messages
dataset_prepared_path: last_run_prepared_medran_uncensored_final
val_set_size: 0.01
output_dir: ./HDD/MedraN_uncensored_final
adapter: lora
# lora_model_dir:
peft_use_rslora: true
sequence_len: 5400
pad_to_sequence_len: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 10
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00004
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
auto_resume_from_checkpoints: true
logging_steps: 1
#flash_attention: true
eager_attention: true
warmup_steps: 50
evals_per_epoch: 2
eval_max_new_tokens: 128
saves_per_epoch: 2
save_total_limit: 100
debug:
weight_decay: 0.0
use_wandb: true
wandb_project: "MedraN-Uncensored"
wandb_name: "MedraN-Uncensored-bf16-stage1-final"
deepspeed: deepspeed_configs/zero1.json
HDD/MedraN_uncensored_final
This model was trained from scratch on the /root/Uncensored_Reasoner_Small_Chat.json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4726
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: 4e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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: 50
- training_steps: 5619
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0 | 0 | 1.9622 |
| 1.1262 | 0.5 | 281 | 1.3529 |
| 1.3188 | 1.0 | 562 | 1.2051 |
| 1.0273 | 1.5 | 843 | 1.1405 |
| 1.0187 | 2.0 | 1124 | 1.0350 |
| 0.6996 | 2.5 | 1405 | 0.9807 |
| 0.8199 | 3.0 | 1686 | 0.8967 |
| 0.6026 | 3.5 | 1967 | 0.8557 |
| 0.6366 | 4.0 | 2248 | 0.8061 |
| 0.6249 | 4.5 | 2529 | 0.7436 |
| 0.3654 | 5.0 | 2810 | 0.6693 |
| 0.3942 | 5.5 | 3091 | 0.6110 |
| 0.2992 | 6.0 | 3372 | 0.5921 |
| 0.5288 | 6.5 | 3653 | 0.5716 |
| 0.4762 | 7.0 | 3934 | 0.5238 |
| 0.3181 | 7.5 | 4215 | 0.5131 |
| 0.3146 | 8.0 | 4496 | 0.4884 |
| 0.2855 | 8.5 | 4777 | 0.4777 |
| 0.3426 | 9.0 | 5058 | 0.4762 |
| 0.2711 | 9.5 | 5339 | 0.4726 |
Framework versions
- PEFT 0.16.0
- Transformers 4.53.2
- Pytorch 2.7.1+cu128
- Datasets 4.0.0
- Tokenizers 0.21.2