llama3-8b-pippa / README.md
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---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: workspace/llama3-8b-pippa
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-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
# - path: taozi555/bagel
# type: sharegpt
- path: MinervaAI/Aesir-Preview
type: sharegpt
- path: KaraKaraWitch/PIPPA-ShareGPT-formatted
type: sharegpt
chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: /workspace/llama3-8b-pippa
adapter: qlora
lora_model_dir:
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
- embed_tokens
- lm_head
wandb_project: waifu
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.0002
optimizer: paged_adamw_32bit
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
#bfloat16: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
eval_steps: 100
eval_table_size:
eval_table_max_new_tokens:
eval_sample_packing: false
saves_per_epoch:
save_steps: 100
save_total_limit: 2
debug:
#deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16_cpuoffload_all.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|im_end|>"
tokens:
- "<|im_start|>"
```
</details><br>
# workspace/llama3-8b-pippa
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: 1.5946
## 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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.6425 | 0.0 | 1 | 4.4372 |
| 1.9054 | 0.21 | 100 | 1.6499 |
| 1.6536 | 0.41 | 200 | 1.6101 |
| 1.7332 | 0.62 | 300 | 1.5973 |
| 1.7975 | 0.82 | 400 | 1.6079 |
| 1.669 | 1.01 | 500 | 1.5992 |
| 1.5612 | 1.21 | 600 | 1.5926 |
| 1.6936 | 1.42 | 700 | 1.5868 |
| 1.6197 | 1.62 | 800 | 1.5707 |
| 1.6831 | 1.83 | 900 | 1.5690 |
| 1.4055 | 2.02 | 1000 | 1.5902 |
| 1.4736 | 2.22 | 1100 | 1.5987 |
| 1.4137 | 2.43 | 1200 | 1.5899 |
| 1.4527 | 2.63 | 1300 | 1.5854 |
| 1.507 | 2.84 | 1400 | 1.5814 |
| 1.4538 | 3.03 | 1500 | 1.5900 |
| 1.4501 | 3.24 | 1600 | 1.5938 |
| 1.3612 | 3.44 | 1700 | 1.5928 |
| 1.4801 | 3.65 | 1800 | 1.5922 |
| 1.3502 | 3.85 | 1900 | 1.5946 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0