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: []

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

Framework versions

• PEFT 0.16.0
• Transformers 4.53.2
• Pytorch 2.7.1+cu128
• Datasets 4.0.0
• Tokenizers 0.21.2