---
license: llama3
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: llama3-8b-hermes-sandals-sample-10k
  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: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: ./data/openhermes2_5_10k.jsonl
    type: sharegpt
    conversation: llama3
dataset_prepared_path: 
val_set_size: 0.15
output_dir: ./outputs_lora-out
hub_model_id: venetis/llama3-8b-hermes-sandals-sample-10k

data_seed: 117
seed: 117

chat_template: llama3
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:


sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: llama-3-8b-hermes-sandals-sample-10k
wandb_entity: venetispall

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 2e-4

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

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


warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
   pad_token: <|end_of_text|>

```

</details><br>

# llama3-8b-hermes-sandals-sample-10k

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7611

## 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: 2
- eval_batch_size: 2
- seed: 117
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.918         | 0.0102 | 1    | 0.9709          |
| 0.7146        | 0.2538 | 25   | 0.7678          |
| 0.728         | 0.5076 | 50   | 0.7419          |
| 0.6843        | 0.7614 | 75   | 0.7328          |
| 0.6819        | 1.0152 | 100  | 0.7259          |
| 0.6342        | 1.2487 | 125  | 0.7269          |
| 0.616         | 1.5025 | 150  | 0.7298          |
| 0.7092        | 1.7563 | 175  | 0.7250          |
| 0.6453        | 2.0102 | 200  | 0.7224          |
| 0.5267        | 2.2411 | 225  | 0.7425          |
| 0.5702        | 2.4949 | 250  | 0.7424          |
| 0.5459        | 2.7487 | 275  | 0.7421          |
| 0.6327        | 3.0025 | 300  | 0.7428          |
| 0.5649        | 3.2335 | 325  | 0.7573          |
| 0.4318        | 3.4873 | 350  | 0.7617          |
| 0.5523        | 3.7411 | 375  | 0.7611          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1