---
library_name: peft
license: other
base_model: facebook/opt-125m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dd1a45ea-8644-4fbc-8112-2f1a5949dce2
  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
accelerate_config:
  dynamo_backend: inductor
  mixed_precision: bf16
  num_machines: 1
  num_processes: auto
  use_cpu: false
adapter: lora
base_model: facebook/opt-125m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - e97c8a5274ad0fb3_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/e97c8a5274ad0fb3_train_data.json
  type:
    field_instruction: src
    field_output: tgt
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: VERSIL91/dd1a45ea-8644-4fbc-8112-2f1a5949dce2
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 20
micro_batch_size: 2
mlflow_experiment_name: /tmp/e97c8a5274ad0fb3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
quantization_config:
  llm_int8_enable_fp32_cpu_offload: true
  load_in_8bit: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: true
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: dd1a45ea-8644-4fbc-8112-2f1a5949dce2
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: dd1a45ea-8644-4fbc-8112-2f1a5949dce2
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# dd1a45ea-8644-4fbc-8112-2f1a5949dce2

This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co./facebook/opt-125m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5669

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB 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
- training_steps: 20

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 50.2344       | 0.0038 | 1    | 2.6428          |
| 42.4766       | 0.0189 | 5    | 2.6323          |
| 46.2188       | 0.0378 | 10   | 2.6029          |
| 39.8281       | 0.0567 | 15   | 2.5738          |
| 38.2188       | 0.0756 | 20   | 2.5669          |


### Framework versions

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