---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 123e4567-e89b-12d3-a456-426614174000
results: []
---
[](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Meta-Llama-3-8B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- format: custom
path: mhenrichsen/alpaca_2k_test
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction}
{input}'
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: false
group_by_length: false
hub_model_id: sylyas/123e4567-e89b-12d3-a456-426614174000
hub_repo: sylyas
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: 500
micro_batch_size: 1
mlflow_experiment_name: mhenrichsen/alpaca_2k_test
model_type: AutoModelForCausalLM
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
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: ilyas
wandb_mode: online
wandb_name: 123e4567-e89b-12d3-a456-426614174000
wandb_project: Public_TuningSN
wandb_run: miner_id_24
wandb_runid: 123e4567-e89b-12d3-a456-426614174000
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
# 123e4567-e89b-12d3-a456-426614174000
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9018
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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: 475
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3764 | 0.0021 | 1 | 1.6561 |
| 0.9225 | 0.2505 | 119 | 0.9177 |
| 1.0708 | 0.5011 | 238 | 0.9003 |
| 1.066 | 0.7516 | 357 | 0.9018 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1