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---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: finetune/output/climate-5day
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
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: Howard881010/climate-5day
type: alpaca
train_on_split: train
dataset_prepared_path:
output_dir: ./finetune/output/climate-5day
test_datasets:
- path: Howard881010/climate-5day
split: valid
type: alpaca
adapter: lora
lora_model_dir:
sequence_len: 3200
sample_packing: false
pad_to_sequence_len: true
lora_r: 8
lora_alpha: 32
lora_dropout: 0.1
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: finetune
wandb_entity:
wandb_watch:
wandb_name: climate-5day
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_hf
learning_rate: 0.00002
max_grad_norm: 1.0
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
logging_steps: 1
xformers_attention:
flash_attention: true
eval_sample_packing: False
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
seed: 0
special_tokens:
pad_token: "<|end_of_text|>"
```
</details><br>
# finetune/output/climate-5day
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0853
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7314 | 0.0028 | 1 | 1.3110 |
| 1.5815 | 0.2514 | 91 | 1.1213 |
| 1.5008 | 0.5028 | 182 | 1.0918 |
| 1.3762 | 0.7541 | 273 | 1.0853 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |