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Model: Magpie-Align/Llama-3-8B-Magpie-Align-v0.3
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---
license: llama3
base_model: Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.3
tags:
- alignment-handbook
- axolotl
- trl
- dpo
- sft
- generated_from_trainer
datasets:
- princeton-nlp/llama3-ultrafeedback-armorm
- Magpie-Align/Magpie-Pro-MT-300K-v0.1
- Magpie-Align/Magpie-Reasoning-150K
- Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese
model-index:
- name: Magpie-Align/Llama-3-8B-Magpie-Align-v0.3
results: []
language:
- en
- zh
---
![Magpie](https://cdn-uploads.huggingface.co/production/uploads/653df1323479e9ebbe3eb6cc/FWWILXrAGNwWr52aghV0S.png)
## 🔥 Chat with Magpie [Here](https://huggingface.co/spaces/flydust/Chat-with-Magpie)!
# 🐦 Llama-3-8B-Magpie-Align-v0.3
Project Web: [https://magpie-align.github.io/](https://magpie-align.github.io/)
Online Model Demo: [https://huggingface.co/spaces/flydust/Chat-with-Magpie](https://huggingface.co/spaces/flydust/Chat-with-Magpie)
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
This model is an aligned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). We apply the following pipeline:
We first perform SFT using:
* [Magpie-Align/Magpie-Pro-MT-300K-v0.1](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-MT-300K-v0.1)
* [Magpie-Align/Magpie-Reasoning-150K](https://huggingface.co/datasets/Magpie-Align/Magpie-Reasoning-150K)
* [Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese)
* **SFT Model Checkpoint:** [Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.3](https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.3)
We then perform DPO on the [princeton-nlp/llama3-ultrafeedback-armorm](https://huggingface.co/datasets/princeton-nlp/llama3-ultrafeedback-armorm) dataset.
The overall performance is much better than the official Llama-3-8B-Instruct Model! Plus, it can answer Chinese queries frequently, thanks to our new [Chinese instruction dataset](https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese)!
- **Alpaca Eval 2 (vs GPT-4-Turbo-1106): 48.58 (LC), 50.36 (WR)**
- **Alpaca Eval 2 (vs Llama-3-8B-Instruct): 73.65 (LC), 75.81 (WR)**
- **Arena Hard: 42.2**
- **WildBench WB-Score: 41.1**
- **Zero-Eval GSM: 50.0**
## 🔥 Model Performance
We compare our Llama-3-8B-Magpie-Align with official and other **open-aligned LLMs** that have been fine-tuned from base models and have publicly released their training datasets. The results are as follows:
```
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| Aligned Model ID | MT-Bench | | | Alpaca Eval 2 | | Alpaca Eval 2 | | Arena Hard |
| | | | | (GPT-4-Turbo-1106) | | (Llama-3-8B-Instruct) | | |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| | R1 | R2 | AVG | LC WR | WR | LC WR | WR | Score |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| meta-llama/Meta-Llama-3-8B-Instruct | 8.31 | 7.65 | 7.98 | 22.92 | 22.57 | 50 | 50 | 20.6 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| princeton-nlp/Llama-3-Base-8B-SFT-DPO | 8.12 | 7.23 | 7.67 | 17.71 | 15.34 | 43.73 | 38.80 | 14.8 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| NousResearch/Hermes-2-Pro-Llama-3-8B | 8.05 | 7.35 | 7.70 | 15.60 | 12.86 | 36.37 | 30.52 | 11.5 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| allenai/llama-3-tulu-2-dpo-8b | 7.71 | 7.15 | 7.43 | 14.89 | 14.8 | 35.43 | 35.42 | 11.7 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| cognitivecomputations/dolphin-2.9-llama3-8b | 7.97 | 6.98 | 7.47 | 12.50 | 8.79 | 32.67 | 22.80 | 8.2 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| openchat/openchat-3.6-8b-20240522 | 7.83 | 7.23 | 7.53 | 17.70 | 12.53 | 41.30 | 30.79 | 6.7 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| Magpie-Align/Llama-3-8B-Magpie-Align-v0.1 | 8.01 | 7.63 | 7.82 | 38.52 | 38.47 | 69.37 | 70.05 | 32.4 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| Magpie-Align/Llama-3-8B-Magpie-Align-v0.2 | 7.81 | 7.64 | 7.73 | 49.86 | 51.98 | 75.17 | 78.20 | 37.5 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
| Magpie-Align/Llama-3-8B-Magpie-Align-v0.3 | 7.82 | 7.51 | 7.67 | 48.58 | 50.36 | 73.65 | 75.81 | 42.2 |
+---------------------------------------------+----------+------+------+--------------------+-------+-----------------------+-------+------------+
```
## 👀 Other Information
**License**: Please follow [Meta Llama 3 Community License](https://llama.meta.com/llama3/license).
**Conversation Template**: Please use Llama 3 **official chat template** for the best performance.
**How to use it?** Please check the official [Llama 3 repository](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct#how-to-use) for detailed instructions. Simply replace the original `model_id` with `Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v1.0`.
The detailed training pipeline is as follows.
## Stage 1: Supervised Fine-tuning
We use [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for SFT.
### 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: 98
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8616 | 0.0019 | 1 | 0.8870 |
| 0.5554 | 0.2013 | 106 | 0.5568 |
| 0.5067 | 0.4027 | 212 | 0.5065 |
| 0.4728 | 0.6040 | 318 | 0.4865 |
| 0.4681 | 0.8054 | 424 | 0.4740 |
| 0.4563 | 1.0067 | 530 | 0.4662 |
| 0.4115 | 1.1944 | 636 | 0.4642 |
| 0.3993 | 1.3957 | 742 | 0.4620 |
| 0.4048 | 1.5971 | 848 | 0.4613 |
| 0.4167 | 1.7984 | 954 | 0.4611 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
*Internal name for identification: Llama-3-8B-Magpie-Mix-RC*. Please change the model name in the below Axolotl config.
[<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: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Magpie-Align/Magpie-Reasoning-150K
type: sharegpt
conversation: llama3
- path: Magpie-Align/Magpie-Qwen2-Pro-200K-Chinese
type: sharegpt
conversation: llama3
- path: Magpie-Align/Magpie-Pro-MT-300K-v0.1
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: axolotl_out/Llama-3-8B-Magpie-Mix-RC
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-8B-Magpie-Mix-RC
wandb_log_model:
hub_model_id: Magpie-Align/Llama-3-8B-Magpie-Mix-RC
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: auto
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>
## Stage 2: Direct Preference Optimization
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5813 | 0.2137 | 100 | 0.5238 | -2.6816 | -3.4539 | 0.7298 | 0.7723 | -612.4234 | -541.2933 | -1.1244 | -1.1082 |
| 0.5021 | 0.4275 | 200 | 0.4483 | -3.4053 | -4.4858 | 0.8024 | 1.0805 | -715.6146 | -613.6641 | -1.1035 | -1.0844 |
| 0.3802 | 0.6412 | 300 | 0.4069 | -3.7974 | -5.1705 | 0.8427 | 1.3731 | -784.0882 | -652.8716 | -1.1310 | -1.1105 |
| 0.3827 | 0.8549 | 400 | 0.3872 | -4.3693 | -5.9670 | 0.8710 | 1.5976 | -863.7308 | -710.0647 | -1.1495 | -1.1283 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
<details><summary>See alignment handbook config</summary>
```yaml
# Customized Configs
model_name_or_path: Magpie-Align/Llama-3-8B-Magpie-Align-SFT-v0.3
hub_model_id: Magpie-Align/Llama-3-8B-Magpie-Align-v0.3-RC
output_dir: alignment_handbook_out/Llama-3-8B-Magpie-Align-v0.3-RC
run_name: Llama-3-8B-Magpie-Align-v0.3-RC
dataset_mixer:
princeton-nlp/llama3-ultrafeedback-armorm: 1.0
dataset_splits:
- train
- test
preprocessing_num_workers: 24
# DPOTrainer arguments
bf16: true
beta: 0.01
learning_rate: 0.7e-6
gradient_accumulation_steps: 8
per_device_train_batch_size: 2
per_device_eval_batch_size: 4
num_train_epochs: 1
max_length: 2048
max_prompt_length: 1800
warmup_ratio: 0.1
logging_steps: 1
lr_scheduler_type: cosine
optim: adamw_torch
torch_dtype: null
use_flash_attention_2: true
do_eval: true
evaluation_strategy: steps
eval_steps: 100
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: False
log_level: info
push_to_hub: true
save_strategy: "steps"
save_steps: 100
save_total_limit: 1
seed: 42
report_to:
- wandb
```
</details><be>
## Downstream Performance (Lighteval)
| Datasets | Llama-3-8B-Magpie-Align-v0.3 |
| :--- | :---: |
| MMLU (5) | 65.69 |
| ARC (25) | 63.23 |
| HellaSwag (25) | 82.15 |
| TruthfulQA (0) | 60.97 |
| Winogrande (5) | 73.64 |
## Paper Abstract
<details><summary>Click Here</summary>
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent existing open-source data creation methods from scaling effectively, potentially limiting the diversity and quality of public alignment datasets. Is it possible to synthesize high-quality instruction data at scale by extracting it directly from an aligned LLM? We present a self-synthesis method for generating large-scale alignment data named Magpie. Our key observation is that aligned LLMs like Llama-3-Instruct can generate a user query when we input only the left-side templates up to the position reserved for user messages, thanks to their auto-regressive nature. We use this method to prompt Llama-3-Instruct and generate 4 million instructions along with their corresponding responses. We perform a comprehensive analysis of the extracted data and select 300K high-quality instances. To compare Magpie data with other public instruction datasets, we fine-tune Llama-3-8B-Base with each dataset and evaluate the performance of the fine-tuned models. Our results indicate that in some tasks, models fine-tuned with Magpie perform comparably to the official Llama-3-8B-Instruct, despite the latter being enhanced with 10 million data points through supervised fine-tuning (SFT) and subsequent feedback learning. We also show that using Magpie solely for SFT can surpass the performance of previous public datasets utilized for both SFT and preference optimization, such as direct preference optimization with UltraFeedback. This advantage is evident on alignment benchmarks such as AlpacaEval, ArenaHard, and WildBench.
</details><be>
## 📚 Citation
If you find the model, data, or code useful, please cite our paper:
```
@article{xu2024magpie,
title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
year={2024},
eprint={2406.08464},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please also cite the creators of preference datasets:
SimPO paper:
```
@article{meng2024simpo,
title={{SimPO}: Simple preference optimization with a reference-free reward},
author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
journal={arXiv preprint arXiv:2405.14734},
year={2024}
}
```
UltraFeedback paper:
```
@article{cui2023ultrafeedback,
title={{UltraFeedback}: Boosting language models with high-quality feedback},
author={Cui, Ganqu and Yuan, Lifan and Ding, Ning and Yao, Guanming and Zhu, Wei and Ni, Yuan and Xie, Guotong and Liu, Zhiyuan and Sun, Maosong},
journal={arXiv preprint arXiv:2310.01377},
year={2023}
}
```
ArmoRM paper:
```
@article{wang2024interpretable,
title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts},
author={Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong},
journal={arXiv preprint arXiv:2406.12845},
year={2024}
}
```
**Questions?** Please contact [Zhangchen](https://zhangchenxu.com/) by email.

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{
"_name_or_path": "Magpie-Align/Llama-3-8B-Magpie-Mix-RC",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": 128001,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.42.3",
"use_cache": true,
"vocab_size": 128256
}

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