---
library_name: peft
license: llama3.1
base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
tags:
- axolotl
- generated_from_trainer
datasets:
- dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl
model-index:
- name: alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000
  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: `0.9.1`
```yaml
base_model: mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: Meta-Llama-3.1-_outputs_axolotl_ft_alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000
output_dir: ./outputs/out/Meta-Llama-3.1-_outputs_axolotl_ft_alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000
hub_model_id: cgifbribcgfbi/alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000

tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false

datasets:
  - path: dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl
    type: chat_template
    field_messages: messages

dataset_prepared_path: last_run_prepared
val_set_size: 0.04
save_safetensors: true

sequence_len: 2700
sample_packing: true
pad_to_sequence_len: true

lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true

wandb_mode:
wandb_project: finetune-sweep
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4  # This will be automatically adjusted based on available GPU memory
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00002

train_on_inputs: false
group_by_length: true
bf16: true
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
logging_steps: 1
flash_attention: true

warmup_steps: 10
evals_per_epoch: 3
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: false
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
special_tokens:
  pad_token: <|finetune_right_pad_id|>
```

</details><br>

# alpha32_r64_lr0.00002_Meta-Llama-3.1-_dset_comp3.0_sortpatent_count_pat400_in5_5000

This model is a fine-tuned version of [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) on the dset_comp3.0_sortpatent_count_pat400_in5_5000.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4583

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- 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: 10
- num_epochs: 4.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7           | 0.0061 | 1    | 0.8766          |
| 0.6414        | 0.3354 | 55   | 0.6293          |
| 0.5608        | 0.6707 | 110  | 0.5473          |
| 0.4733        | 1.0061 | 165  | 0.5161          |
| 0.5142        | 1.3415 | 220  | 0.4954          |
| 0.4771        | 1.6768 | 275  | 0.4824          |
| 0.423         | 2.0122 | 330  | 0.4750          |
| 0.4375        | 2.3476 | 385  | 0.4676          |
| 0.4311        | 2.6829 | 440  | 0.4630          |
| 0.4019        | 3.0183 | 495  | 0.4620          |
| 0.4726        | 3.3537 | 550  | 0.4589          |
| 0.4677        | 3.6890 | 605  | 0.4583          |


### Framework versions

- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1