README.md

---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 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
adapter: lora
base_model: unsloth/Llama-3.2-3B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - /workspace/input_data/alpaca_2k_test_train_data.json
  ds_type: json
  path: /workspace/input_data/alpaca_2k_test_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    field_system: text
    format: "User: {instruction} {input} \n Assistant: "
    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: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: cwaud/test
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/alpaca_2k_test_train_data.json
model_type: LlamaForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 5
save_strategy: steps
sequence_len: 4096
special_tokens:
  pad_token: ' '
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: rayonlabs-rayon-labs
wandb_mode: online
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: a6f2d070-3c63-4112-ba45-7a0b6682befe
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# test

This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1921

## 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
- training_steps: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.0056        | 0.0047 | 1    | 4.8819          |
| 5.0172        | 0.0140 | 3    | 4.4316          |
| 3.0436        | 0.0281 | 6    | 1.5273          |
| 0.1707        | 0.0421 | 9    | 0.1921          |


### Framework versions

- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0