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---
library_name: transformers
base_model: nvidia/Llama-3.1-Minitron-4B-Width-Base
tags:
- axolotl
- generated_from_trainer
model-index:
- name: MagpieLM-4B-SFT-v0.1
results: []
datasets:
- Magpie-Align/MagpieLM-SFT-Data-v0.1
language:
- en
---
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://api.wandb.ai/links/uw-nsl/7grozq8s)
# 🐦 MagpieLM-4B-SFT-v0.1
Project Web: [https://magpie-align.github.io/](https://magpie-align.github.io/)
Arxiv Technical Report: [https://arxiv.org/abs/2406.08464](https://arxiv.org/abs/2406.08464)
Codes: [https://github.com/magpie-align/magpie](https://github.com/magpie-align/magpie)
## About This Model
*Model full name: Llama3.1-MagpieLM-4B-SFT-v0.1*
This model is a fine-tuned version of [nvidia/Llama-3.1-Minitron-4B-Width-Base](https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Width-Base) on [Magpie-Align/MagpieLM-SFT-Data-v0.1](https://huggingface.co/datasets/Magpie-Align/MagpieLM-SFT-Data-v0.1) dataset.
This is the intermediate checkpoint for fine-tuning [Magpie-Align/MagpieLM-4B-Chat-v0.1](https://huggingface.co/Magpie-Align/MagpieLM-4B-Chat-v0.1).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 51
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1026 | 0.0038 | 1 | 1.1547 |
| 0.6994 | 0.2015 | 53 | 0.7142 |
| 0.6181 | 0.4030 | 106 | 0.6375 |
| 0.5967 | 0.6045 | 159 | 0.6134 |
| 0.5793 | 0.8060 | 212 | 0.6004 |
| 0.5736 | 1.0075 | 265 | 0.5914 |
| 0.5411 | 1.1938 | 318 | 0.5883 |
| 0.5402 | 1.3953 | 371 | 0.5864 |
| 0.5423 | 1.5968 | 424 | 0.5856 |
| 0.5408 | 1.7983 | 477 | 0.5854 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
[<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: nvidia/Llama-3.1-Minitron-4B-Width-Base
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
chat_template: llama3
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Magpie-Align/MagpieLM-SFT-Data-v0.1
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: axolotl_out/MagpieLM-4B-SFT-v0.1
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama3.1-MagpieLM-4B-SFT-v0.1
wandb_log_model:
hub_model_id: Magpie-Align/MagpieLM-4B-SFT-v0.1
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br> |