---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-1B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: miner_id_1_383a850e-bb15-45a2-8f4b-fc96eb001a74_1729712965
results: []
---
[](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Llama-3.2-1B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
debug: null
deepspeed: null
early_stopping_patience: 10
eval_max_new_tokens: 128
eval_steps: 20
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: besimray/miner_id_1_383a850e-bb15-45a2-8f4b-fc96eb001a74_1729712965
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: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10000
micro_batch_size: 10
mlflow_experiment_name: mhenrichsen/alpaca_2k_test
model_type: LlamaForCausalLM
num_epochs: 100
optimizer: adamw_bnb_8bit
output_dir: miner_id_besimray
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 20
save_strategy: steps
sequence_len: 4096
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: besimray24-rayon
wandb_mode: online
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: 383a850e-bb15-45a2-8f4b-fc96eb001a74
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
```
# miner_id_1_383a850e-bb15-45a2-8f4b-fc96eb001a74_1729712965
This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5010
## 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: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 4750
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2983 | 0.0211 | 1 | 1.2586 |
| 1.3601 | 0.4211 | 20 | 1.1757 |
| 1.2034 | 0.8421 | 40 | 1.1567 |
| 1.1302 | 1.2632 | 60 | 1.1534 |
| 1.0958 | 1.6842 | 80 | 1.1512 |
| 1.0285 | 2.1053 | 100 | 1.1653 |
| 1.1265 | 2.5263 | 120 | 1.1785 |
| 1.0215 | 2.9474 | 140 | 1.1921 |
| 0.8495 | 3.3684 | 160 | 1.2673 |
| 0.901 | 3.7895 | 180 | 1.2611 |
| 0.7058 | 4.2105 | 200 | 1.3737 |
| 0.7428 | 4.6316 | 220 | 1.3824 |
| 0.4866 | 5.0526 | 240 | 1.4475 |
| 0.5298 | 5.4737 | 260 | 1.5484 |
| 0.5671 | 5.8947 | 280 | 1.5010 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1