---
library_name: transformers
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
tags:
- axolotl
- generated_from_trainer
datasets:
- argilla/databricks-dolly-15k-curated-en
model-index:
- name: tiny-random-LlamaForCausalLM
  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: `0.6.0`
```yaml
base_model: trl-internal-testing/tiny-random-LlamaForCausalLM
batch_size: 32
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- format: custom
  path: argilla/databricks-dolly-15k-curated-en
  type:
    field_input: original-instruction
    field_instruction: original-instruction
    field_output: original-response
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
device_map: auto
eval_sample_packing: false
eval_steps: 200
flash_attention: true
gpu_memory_limit: 80GiB
group_by_length: true
hub_model_id: SystemAdmin123/tiny-random-LlamaForCausalLM
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 2500
micro_batch_size: 4
model_type: AutoModelForCausalLM
num_epochs: 100
optimizer: adamw_bnb_8bit
output_dir: /root/.sn56/axolotl/outputs/tiny-random-LlamaForCausalLM
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: false
save_steps: 400
save_total_limit: 1
sequence_len: 2048
tokenizer_type: LlamaTokenizerFast
torch_dtype: bf16
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: trl-internal-testing/tiny-random-LlamaForCausalLM-argilla/databricks-dolly-15k-curated-en
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05

```

</details><br>

# tiny-random-LlamaForCausalLM

This model is a fine-tuned version of [trl-internal-testing/tiny-random-LlamaForCausalLM](https://huggingface.co/trl-internal-testing/tiny-random-LlamaForCausalLM) on the argilla/databricks-dolly-15k-curated-en dataset.
It achieves the following results on the evaluation set:
- Loss: 8.6989

## 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
- 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: 125
- training_steps: 2500

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0003 | 1    | 10.3763         |
| 9.7054        | 0.0592 | 200  | 9.6862          |
| 8.9091        | 0.1184 | 400  | 8.9612          |
| 8.7257        | 0.1776 | 600  | 8.7627          |
| 8.7416        | 0.2368 | 800  | 8.7109          |
| 8.5944        | 0.2959 | 1000 | 8.6982          |
| 8.673         | 0.3551 | 1200 | 8.6963          |
| 8.7511        | 0.4143 | 1400 | 8.6972          |
| 8.729         | 0.4735 | 1600 | 8.6961          |
| 8.6325        | 0.5327 | 1800 | 8.6948          |
| 8.6338        | 0.5919 | 2000 | 8.6946          |
| 8.7376        | 0.6511 | 2200 | 8.6954          |
| 8.573         | 0.7103 | 2400 | 8.6989          |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.4.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0