---
library_name: peft
license: apache-2.0
base_model: EleutherAI/pythia-410m-deduped
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e74610a7-8302-49f8-852a-453d0769bacc
  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: `0.4.1`
```yaml
adapter: qlora
base_model: EleutherAI/pythia-410m-deduped
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 11866109baf7431c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/11866109baf7431c_train_data.json
  type:
    field_input: Chapter
    field_instruction: Book Name
    field_output: Chunk
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: true
hub_model_id: error577/e74610a7-8302-49f8-852a-453d0769bacc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
micro_batch_size: 4
mlflow_experiment_name: /tmp/11866109baf7431c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
restore_best_weights: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 200
sequence_len: 1024
special_tokens:
  pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: 22419f01-216c-4f29-9237-04f8efc9e9b9
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 22419f01-216c-4f29-9237-04f8efc9e9b9
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# e74610a7-8302-49f8-852a-453d0769bacc

This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9558

## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 60.3727       | 0.0018 | 1    | 3.5860          |
| 53.9221       | 0.3581 | 200  | 3.1653          |
| 52.424        | 0.7163 | 400  | 3.0718          |
| 47.3986       | 1.0744 | 600  | 3.0114          |
| 46.1675       | 1.4326 | 800  | 2.9953          |
| 47.8651       | 1.7907 | 1000 | 2.9769          |
| 47.7323       | 2.1489 | 1200 | 2.9623          |
| 46.0662       | 2.5070 | 1400 | 2.9582          |
| 45.702        | 2.8651 | 1600 | 2.9558          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1