---
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
library_name: peft
license: apache-2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: axolotl-test
  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

# Model config
adapter: qlora
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
# base_model: meta-llama/Llama-3.2-3B
bf16: auto

# HF hub config (push to huggingface)
# requires HF_TOKEN api key to be set (👈🔑secrets)
hf_use_auth_token: true
hub_model_id: mgfrantz/axolotl-test
mlflow_experiment_name: axolotl-test

# # Data config
dataset_prepared_path: data
chat_template: chatml
datasets:
  - path: data/train.jsonl
    ds_type: json
    data_files:
      - data/train.jsonl
    conversation: alpaca
    type: sharegpt

test_datasets:
  - path: data/eval.jsonl
    ds_type: json
    # You need to specify a split. For "json" datasets the default split is called "train".
    split: train
    type: sharegpt
    conversation: alpaca
    data_files:
      - data/eval.jsonl


# Training config
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: false
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false


learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
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: 32
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
micro_batch_size: 8
model_type: LlamaForCausalLM
num_epochs: 4
optimizer: paged_adamw_32bit
output_dir: ./outputs/qlora-out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sequence_len: 4096
special_tokens: null
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# axolotl-test

This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4338

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.4962        | 0.5714 | 1    | 2.4779          |
| 5.3564        | 1.0714 | 2    | 2.4760          |
| 4.3272        | 1.6429 | 3    | 2.4633          |
| 4.7348        | 2.1429 | 4    | 2.4338          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1