---
base_model: concedo/KobbleTinyV2-1.1B
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: outputs/32r
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: concedo/KobbleTinyV2-1.1B
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: NobodyExistsOnTheInternet/AlpacaToxicQA
    type: alpaca
  - path: Fischerboot/freedom-rp-alpaca-shortend
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/32r

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# outputs/32r

This model is a fine-tuned version of [concedo/KobbleTinyV2-1.1B](https://huggingface.co/concedo/KobbleTinyV2-1.1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3368

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.9821        | 0.0034 | 1    | 1.8932          |
| 1.6851        | 0.2517 | 73   | 1.5089          |
| 1.4335        | 0.5034 | 146  | 1.4387          |
| 1.3165        | 0.7552 | 219  | 1.4085          |
| 2.0848        | 1.0069 | 292  | 1.3896          |
| 1.3564        | 1.2379 | 365  | 1.3757          |
| 1.2587        | 1.4897 | 438  | 1.3640          |
| 1.2955        | 1.7414 | 511  | 1.3552          |
| 1.4962        | 1.9931 | 584  | 1.3487          |
| 1.3458        | 2.2284 | 657  | 1.3455          |
| 1.301         | 2.4802 | 730  | 1.3413          |
| 1.2458        | 2.7319 | 803  | 1.3389          |
| 1.1965        | 2.9836 | 876  | 1.3367          |
| 1.4968        | 3.2172 | 949  | 1.3369          |
| 1.2504        | 3.4690 | 1022 | 1.3368          |
| 1.5103        | 3.7207 | 1095 | 1.3368          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1