---
license: other
base_model: meta-llama/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Red_Llama_3_base
  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
hub_model_id:  KolaGang/Red_Llama_3_base
hub_strategy: end
load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Drewskidang/chatlaw
    type: sharegpt
    conversation: chatml
  - path: Drewskidang/tool
    type: sharegpt
    conversation: chatml
  - path: digitalpipelines/samantha-1.1-uncensored
    type: sharegpt
    conversation: chatml
  - path: KolaGang/mergers
    type: alpaca
 
 



dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

wandb_project: Legal_Llama 
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-4

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero1.json # multi-gpu only
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>
```

</details><br>

# Red_Llama_3_base

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: 0.8173

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1958        | 0.03  | 1    | 1.1846          |
| 1.0515        | 0.49  | 19   | 1.0706          |
| 0.952         | 0.99  | 38   | 0.9385          |
| 0.9038        | 1.44  | 57   | 0.8796          |
| 0.8679        | 1.94  | 76   | 0.8469          |
| 0.7675        | 2.39  | 95   | 0.8280          |
| 0.7643        | 2.88  | 114  | 0.8173          |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0