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Llama-3-Instruct-LORA

PEFT
Safetensors
llama
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Llama-3-Instruct-LORA
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---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: mathvi/output_model2
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false
 
datasets:
  - path: /workspace/axolotl/mathvi/input_output_meta_llama_3_8b_instruct-00000-of-00001.parquet
    type: input_output
dataset_prepared_path:
val_set_size: 0.05
eval_sample_packing: false
output_dir: mathvi/output_model2

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false

adapter: lora
lora_model_dir: 
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 32
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-4

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention: 
flash_attention: true
s2_attention:

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
evals_per_epoch: 10
eval_table_size:
eval_max_new_tokens: 512
saves_per_epoch: 2
save_total_limit: 20
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

# mathvi/output_model2

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3327

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0442        | 0.0190 | 1    | 2.0734          |
| 1.449         | 0.1137 | 6    | 1.2774          |
| 0.8548        | 0.2275 | 12   | 0.9006          |
| 0.8561        | 0.3412 | 18   | 0.7924          |
| 0.744         | 0.4550 | 24   | 0.7176          |
| 0.6752        | 0.5687 | 30   | 0.6603          |
| 0.5908        | 0.6825 | 36   | 0.6117          |
| 0.5229        | 0.7962 | 42   | 0.5702          |
| 0.558         | 0.9100 | 48   | 0.5281          |
| 0.4343        | 1.0237 | 54   | 0.4752          |
| 0.4039        | 1.1374 | 60   | 0.4152          |
| 0.3744        | 1.2512 | 66   | 0.4225          |
| 0.3313        | 1.3649 | 72   | 0.3852          |
| 0.374         | 1.4787 | 78   | 0.3740          |
| 0.3246        | 1.5924 | 84   | 0.3657          |
| 0.3392        | 1.7062 | 90   | 0.3591          |
| 0.3309        | 1.8199 | 96   | 0.3505          |
| 0.3621        | 1.9336 | 102  | 0.3437          |
| 0.2819        | 2.0474 | 108  | 0.3416          |
| 0.2672        | 2.1611 | 114  | 0.3414          |
| 0.2284        | 2.2749 | 120  | 0.3375          |
| 0.2836        | 2.3886 | 126  | 0.3353          |
| 0.2504        | 2.5024 | 132  | 0.3337          |
| 0.2696        | 2.6161 | 138  | 0.3328          |
| 0.2775        | 2.7299 | 144  | 0.3327          |
| 0.2554        | 2.8436 | 150  | 0.3325          |
| 0.2551        | 2.9573 | 156  | 0.3327          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1