---
library_name: peft
license: apache-2.0
base_model: unsloth/codegemma-2b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9c8ac561-b519-4a02-bf7b-a8ede55359b3
  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: lora
base_model: unsloth/codegemma-2b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - e1c400429355d086_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/e1c400429355d086_train_data.json
  type:
    field_instruction: instruction
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
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: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Nexspear/9c8ac561-b519-4a02-bf7b-a8ede55359b3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 72GB
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: /tmp/e1c400429355d086_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: 9c8ac561-b519-4a02-bf7b-a8ede55359b3
wandb_project: Gradients-On-Four
wandb_run: your_name
wandb_runid: 9c8ac561-b519-4a02-bf7b-a8ede55359b3
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 9c8ac561-b519-4a02-bf7b-a8ede55359b3

This model is a fine-tuned version of [unsloth/codegemma-2b](https://huggingface.co/unsloth/codegemma-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5511

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: 50

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0019 | 1    | 2.1887          |
| 2.1981        | 0.0094 | 5    | 2.0904          |
| 2.0794        | 0.0188 | 10   | 1.9135          |
| 1.7655        | 0.0282 | 15   | 1.7979          |
| 1.7978        | 0.0376 | 20   | 1.7175          |
| 1.7438        | 0.0470 | 25   | 1.6555          |
| 1.5911        | 0.0564 | 30   | 1.6130          |
| 1.5506        | 0.0658 | 35   | 1.5798          |
| 1.592         | 0.0752 | 40   | 1.5614          |
| 1.5254        | 0.0846 | 45   | 1.5524          |
| 1.5266        | 0.0940 | 50   | 1.5511          |


### Framework versions

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