README.md

---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2b60f15a-e2bc-4ba0-9628-ecf0b960e3ac
  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: true
bnb_config_kwargs:
  bnb_4bit_quant_type: nf4
  bnb_4bit_use_double_quant: true
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - alpaca-cleaned_train_data.json
  ds_type: json
  path: /workspace/input_data/alpaca-cleaned_train_data.json
  type:
    field_input: input
    field_instruction: instruction
    field_output: output
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map:
  ? ''
  : cuda:0
do_eval: false
early_stopping_patience: null
eval_batch_size: 6
eval_sample_packing: false
eval_steps: 0
evaluation_strategy: 'no'
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 10
gradient_checkpointing: true
group_by_length: true
hub_model_id: cwaud/2b60f15a-e2bc-4ba0-9628-ecf0b960e3ac
hub_repo: cwaud
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
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
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 76GiB
max_steps: 400
micro_batch_size: 6
mlflow_experiment_name: /tmp/alpaca-cleaned_train_data.json
model_type: UnknownForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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_strategy: epoch
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
val_set_size: 50
wandb_entity: rayonlabs-rayon-labs
wandb_mode: online
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: 2b60f15a-e2bc-4ba0-9628-ecf0b960e3ac
warmup_raio: 0.03
warmup_ratio: 0.03
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 2b60f15a-e2bc-4ba0-9628-ecf0b960e3ac

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.

## 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: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 10
- total_train_batch_size: 240
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 12
- training_steps: 400

### Training results



### Framework versions

- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1