---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.12.0.dev0`
```yaml
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

```

</details><br>

# 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