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--- ---
license: Apache License 2.0 license: llama3.1
language:
#model-type: - en
##如 gpt、phi、llama、chatglm、baichuan 等 pipeline_tag: text-generation
#- gpt datasets:
- allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
#domain: base_model:
##如 nlp、cv、audio、multi-modal - allenai/Llama-3.1-Tulu-3-8B-DPO
#- nlp library_name: transformers
#language:
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
#- cn
#metrics:
##如 CIDEr、Blue、ROUGE 等
#- CIDEr
#tags:
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
#- pretrained
#tools:
##如 vllm、fastchat、llamacpp、AdaSeq 等
#- vllm
--- ---
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
SDK下载 <img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu3/Tulu3-logo.png" alt="Tulu 3 banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Llama-3.1-Tulu-3.1-8B
Tülu 3 is a leading instruction following model family, offering a post-training package with fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern techniques.
This is one step of a bigger process to training fully open-source models, like our [OLMo](https://allenai.org/olmo) models.
Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval.
**Version 3.1 update**: The new version of our Tülu model is from an improvement only in the final RL stage of training.
We switched from PPO to GRPO (no reward model) and did further hyperparameter tuning to achieve substantial performance improvements across the board over the original Tülu 3 8B model,
as shown in the comparison below:
![](Comparison.png)
## Model description
- **Model type:** A model trained on a mix of publicly available, synthetic and human-created datasets.
- **Language(s) (NLP):** Primarily English
- **License:** Llama 3.1 Community License Agreement
- **Finetuned from model:** allenai/Llama-3.1-Tulu-3-8B-DPO
### Model Sources
- **Training Repository:** https://github.com/allenai/open-instruct
- **Eval Repository:** https://github.com/allenai/olmes
- **Paper:** https://arxiv.org/abs/2411.15124
- **Demo:** https://playground.allenai.org/
### Model Family
| **Stage** | **Llama 3.1 8B (New)** | **Llama 3.1 70B** |
|----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
| **Base Model** | [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) |
| **SFT** | [allenai/Llama-3.1-Tulu-3-8B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-SFT) | [allenai/Llama-3.1-Tulu-3-70B-SFT](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-SFT) |
| **DPO** | [allenai/Llama-3.1-Tulu-3-8B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-DPO) | [allenai/Llama-3.1-Tulu-3-70B-DPO](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B-DPO) |
| **Final Models (RLVR)** | [allenai/Llama-3.1-Tulu-3.1-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) | [allenai/Llama-3.1-Tulu-3-70B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-70B) |
| **Reward Model (RM)**| None with GRPO | [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM) |
| **Stage** | **Llama 3.1 405B** |
|-----------|-------------------|
| **Base Model** | [meta-llama/llama-3.1-405B](https://huggingface.co/meta-llama/llama-3.1-405B) |
| **SFT** | [allenai/llama-3.1-Tulu-3-405B-SFT](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-SFT) |
| **DPO** | [allenai/llama-3.1-Tulu-3-405B-DPO](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B-DPO) |
| **Final Model (RLVR)** | [allenai/llama-3.1-Tulu-3-405B](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B) |
| **Reward Model (RM)**| (Same as 70B)
## Using the model
### Loading with HuggingFace
To load the model with HuggingFace, use the following snippet:
```
from transformers import AutoModelForCausalLM
tulu_model = AutoModelForCausalLM.from_pretrained("allenai/Llama-3.1-Tulu-3.1-8B")
```
### VLLM
As a Llama base model, the model can be easily served with:
```
vllm serve allenai/Llama-3.1-Tulu-3.1-8B
```
Note that given the long chat template of Llama, you may want to use `--max_model_len=8192`.
### Chat template
The chat template for our models is formatted as:
```
<|user|>\nHow are you doing?\n<|assistant|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>
```
Or with new lines expanded:
```
<|user|>
How are you doing?
<|assistant|>
I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>
```
It is embedded within the tokenizer as well, for `tokenizer.apply_chat_template`.
### System prompt
In Ai2 demos, we use this system prompt by default:
```
You are Tulu 3, a helpful and harmless AI Assistant built by the Allen Institute for AI.
```
The model has not been trained with a specific system prompt in mind.
### Bias, Risks, and Limitations
The Tülu3 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base Llama 3.1 models, however it is likely to have included a mix of Web data and technical sources like books and code.
See the Falcon 180B model card for an example of this.
## Performance
| Benchmark (eval) | Tülu 3 SFT 8B | Tülu 3 DPO 8B | Tülu 3 8B | **Tülu 3.1 8B (NEW)** | Llama 3.1 8B Instruct | Qwen 2.5 7B Instruct | Magpie 8B | Gemma 2 9B Instruct | Ministral 8B Instruct |
|---------------------------------|--------------|--------------|-----------|------------|------------------------|----------------------|-----------|---------------------|-----------------------|
| **Avg.** | 60.4 | 64.4 | 64.8 | 66.3 | 62.2 | **66.5** | 44.7 | 55.2 | 58.3 |
| **MMLU (0 shot, CoT)** | 65.9 | 68.7 | 68.2 | 69.5 | 71.2 | **76.6** | 62.0 | 74.6 | 68.5 |
| **PopQA (15 shot)** | **29.3** | 29.3 | 29.1 | 30.2 | 20.2 | 18.1 | 22.5 | 28.3 | 20.2 |
| **TruthfulQA (6 shot)** | 46.8 | 56.1 | 55.0 | 59.9 | 55.1 | **63.1** | 57.0 | 61.4 | 55.5 |
| **BigBenchHard (3 shot, CoT)** | **67.9** | 65.8 | 66.0 | 68.9 | 62.8 | 70.2 | 0.9 | 2.5 | 56.2 |
| **DROP (3 shot)** | 61.3 | 62.5 | 62.6 | **63.9** | 61.5 | 54.4 | 49.4 | 58.8 | 56.2 |
| **MATH (4 shot CoT, Flex)** | 31.5 | 42.0 |43.7 | 47.8 | 42.5 | **69.9** | 5.1 | 29.8 | 40.0 |
| **GSM8K (8 shot, CoT)** | 76.2 | 84.3 | 87.6 | **90.0** | 83.4 | 83.8 | 61.2 | 79.7 | 80.0 |
| **HumanEval (pass@10)** | 86.2 | 83.9 | 83.9 | 84.8 | 86.3 | **93.1** | 75.4 | 71.7 | 91.0 |
| **HumanEval+ (pass@10)** | 81.4 | 78.6 | 79.2 | 80.4 | 82.9 | **89.7** | 69.1 | 67.0 | 88.5 |
| **IFEval (prompt loose)** | 72.8 | 81.1 | 82.4 | **83.9** | 80.6 | 74.7 | 38.8 | 69.9 | 56.4 |
| **AlpacaEval 2 (LC % win)** | 12.4 | 33.5 | 34.5 | 34.9 | 24.2 | 29.0 | **49.0** | 43.7 | 31.4 |
| **Safety (6 task avg.)** | **93.1** | 87.2 | 85.5 | 81.2 | 75.2 | 75.0 | 46.4 | 75.5 | 56.2 |
*Note, see the updated version of the paper for the latest, fixed evaluations that improve scores for models such as Qwen 2.5 Instruct.*
| Benchmark (eval) | Tülu 3 70B SFT | Tülu 3 DPO 70B | Tülu 3 70B | Llama 3.1 70B Instruct | Qwen 2.5 72B Instruct | Hermes 3 Llama 3.1 70B | Nemotron Llama 3.1 70B |
|---------------------------------|-----------------|-----------------|-------------|-------------------------|-----------------------|------------------------|-------------------------|
| **Avg.** | 72.6 | 75.9 | **76.0** | 73.4 | 71.5 | 68.3 | 65.5 |
| **MMLU (0 shot, CoT)** | 78.9 | 83.3 | 83.1 | 85.3 | **85.5** | 80.4 | 83.8 |
| **PopQA (15 shot)** | **48.6** | 46.3 | 46.5 | 46.4 | 30.6 | 48.1 | 36.4 |
| **TruthfulQA (6 shot)** | 55.7 | 67.9 | 67.6 | 66.8 | **69.9** | 66.5 | 62.6 |
| **BigBenchHard (3 shot, CoT)** | **82.7** | 81.8 | 82.0 | 73.8 | 67.2 | 82.1 | 0.7 |
| **DROP (3 shot)** | **77.2** | 74.1 | 74.3 | 77.0 | 34.2 | 73.2 | 68.8 |
| **MATH (4 shot CoT, Flex)** | 53.7 | 62.3 | 63.0 | 56.4 | **74.3** | 41.9 | 55.0 |
| **GSM8K (8 shot, CoT)** | 91.1 | 93.5 | 93.5 | **93.7** | 89.5 | 90.0 | 84.7 |
| **HumanEval (pass@10)** | 92.9 | 92.4 | 92.4 | 93.6 | 94.0 | 89.6 | **94.1** |
| **HumanEval+ (pass@10)** | 87.3 | 88.4 | 88.0 | 89.5 | **90.8** | 85.9 | 85.5 |
| **IFEval (prompt loose)** | 82.1 | 82.6 | 83.2 | **88.0** | 87.6 | 76.0 | 79.9 |
| **AlpacaEval 2 (LC % win)** | 26.3 | 49.6 | 49.8 | 33.4 | 47.7 | 28.4 | **66.1** |
| **Safety (6 task avg.)** | **94.4** | 89.0 | 88.3 | 76.5 | 87.0 | 57.9 | 69.0 |
| Benchmark (eval) | Tülu 3 405B SFT | Tülu 3 405B DPO | Tülu 3 405B | Llama 3.1 405B Instruct | Nous Hermes 3 405B | Deepseek V3 | GPT 4o (11-24) |
|-----------------|----------------|----------------|-------------|------------------------|-------------------|-------------|----------------|
| **Avg w/o Safety** | 76.3 | 79.0 | 80.0 | 78.1 | 74.4 | 79.0 | **80.5** |
| **Avg w/ Safety** | 77.5 | 79.6 | 80.7 | 79.0 | 73.5 | 75.9 | **81.6** |
| **MMLU (5 shot, CoT)** | 84.4 | 86.6 | 87.0 | **88.0** | 84.9 | 82.1 | 87.9 |
| **PopQA (3 shot)** | **55.7** | 55.4 | 55.5 | 52.9 | 54.2 | 44.9 | 53.6 |
| **BigBenchHard (0 shot, CoT)** | 88.0 | 88.8 | 88.6 | 87.1 | 87.7 | **89.5** | 83.3 |
| **MATH (4 shot, Flex)** | 63.4 | 59.9 | 67.3 | 66.6 | 58.4 | **72.5** | 68.8 |
| **GSM8K (8 shot, CoT)** | 93.6 | 94.2 | **95.5** | 95.4 | 92.7 | 94.1 | 91.7 |
| **HumanEval (pass@10)** | 95.7 | **97.2** | 95.9 | 95.9 | 92.3 | 94.6 | 97.0 |
| **HumanEval+ (pass@10)** | 93.3 | **93.9** | 92.9 | 90.3 | 86.9 | 91.6 | 92.7 |
| **IFEval (prompt loose)** | 82.4 | 85.0 | 86.0 | **88.4** | 81.9 | 88.0 | 84.8 |
| **AlpacaEval 2 (LC % win)** | 30.4 | 49.8 | 51.4 | 38.5 | 30.2 | 53.5 | **65.0** |
| **Safety (6 task avg.)** | 87.7 | 85.5 | 86.7 | 86.8 | 65.8 | 72.2 | **90.9** |
## Hyperparamters
GRPO settings for RLVR:
- **Learning Rate**: 5 × 10⁻⁷
- **Discount Factor (gamma)**: 1.0
- **Mini-batches (N_mb)**: 2
- **PPO-style Update Iteration (K)**: 1
- **Clipping Coefficient (epsilon)**: 0.2
- **Gradient Norm Threshold**: 1.0
- **Learning Rate Schedule**: Constant
- **Generation Temperature**: 1.0
- **Number of Samples per Prompt**: 16
- **Number of Unique Prompts per Training Iteration**: 48
- **Batch Size (effective)**: 48 * 16 = 768
- **Max Token Length**: 2,048
- **Max Prompt Token Length**: 2,048
- **Penalty Reward Value for Responses without an EOS Token**: 0.0
- **Response Length**: 2,048
- **Total Episodes**: 10,000,000 (the actual checkpoint is at episode 1474560)
- **KL penalty coefficient (beta)**: 0.01
- **Warm up ratio (omega)**: 0.0
## Learning curves
Below is the training curves for Llama-3.1-Tulu-3.1-8B:
![](Llama-3.1-Tulu-3.1-8B-learning-curve.png)
Below are the core eval scores over steps for Llama-3.1-Tulu-3.1-8B (note we took step `1920` as the final checkpoint, corresponding to episode `1,474,560`):
![](Llama-3.1-Tulu-3.1-8B-core-evals-overtime.png)
Below are the other eval scores over steps for Llama-3.1-Tulu-3.1-8B (the codex evals had a bug and earlier scores are not shown):
![](Llama-3.1-Tulu-3.1-8B-other-evals-overtime.png)
## Reproduction command
```bash ```bash
#安装ModelScope # clone and check out commit
pip install modelscope git clone https://github.com/allenai/open-instruct.git
``` git checkout 3f37c29ddc97d2c108a7658692d2d2c3708ef182
```python
#SDK模型下载 # run my exact command for launching exps
from modelscope import snapshot_download for learning_rate in 5e-7; do
model_dir = snapshot_download('allenai/Llama-3.1-Tulu-3.1-8B') for beta in 0.01; do
``` for nspp in 16; do
Git下载 for m in half-m ; do
``` for kl_estimator in kl3; do
#Git模型下载 local_rollout_batch_size=8
git clone https://www.modelscope.cn/allenai/Llama-3.1-Tulu-3.1-8B.git # `half-m` is the same as setting number of mini-batches to be 2.
if [ $m == "half-m" ]; then
local_mini_batch_size=$(($local_rollout_batch_size * $nspp / 2))
else
local_mini_batch_size=$(($local_rollout_batch_size * $nspp))
fi
exp_name="0204_lr_scan_grpo_math_lr_${learning_rate}_${kl_estimator}_${beta}_${nspp}_${m}_${RANDOM}"
echo $exp_name:
echo --- local_mini_batch_size=$local_mini_batch_size
echo --- num_gradient_updates=$(($local_rollout_batch_size * $nspp / $local_mini_batch_size))
python open_instruct/grpo_vllm_thread_ray_gtrl.py \
--exp_name $exp_name \
--beta $beta \
--local_mini_batch_size $local_mini_batch_size \
--number_samples_per_prompt $nspp \
--output_dir output/$exp_name \
--local_rollout_batch_size $local_rollout_batch_size \
--kl_estimator $kl_estimator \
--learning_rate $learning_rate \
--dataset_mixer_list allenai/RLVR-GSM-MATH-IF-Mixed-Constraints 1.0 \
--dataset_mixer_list_splits train \
--dataset_mixer_eval_list allenai/RLVR-GSM-MATH-IF-Mixed-Constraints 16 \
--dataset_mixer_eval_list_splits train \
--max_token_length 2048 \
--max_prompt_token_length 2048 \
--response_length 2048 \
--model_name_or_path allenai/Llama-3.1-Tulu-3-8B-DPO \
--non_stop_penalty \
--stop_token eos \
--temperature 1.0 \
--ground_truths_key ground_truth \
--chat_template_name tulu \
--sft_messages_key messages \
--total_episodes 10000000 \
--penalty_reward_value 0.0 \
--deepspeed_stage 2 \
--per_device_train_batch_size 2 \
--local_rollout_forward_batch_size 2 \
--actor_num_gpus_per_node 6 \
--num_epochs 1 \
--vllm_tensor_parallel_size 2 \
--lr_scheduler_type constant \
--apply_verifiable_reward true \
--seed 1 \
--num_evals 30 \
--save_freq 40 \
--reward_model_multiplier 0.0 \
--gradient_checkpointing \
--with_tracking
done
done
done
done
done
``` ```
<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p> ## License and use
All Llama 3.1 Tülu3 models are released under Meta's [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/).
Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc.
Tülu3 is intended for research and educational use.
For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
The models have been fine-tuned using a dataset mix with outputs generated from third party models and are subject to additional terms:
[Gemma Terms of Use](https://ai.google.dev/gemma/terms) and [Qwen License Agreement](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE) (models were improved using Qwen 2.5).
## Citation
If Tülu3 or any of the related materials were helpful to your work, please cite:
```
@article{lambert2024tulu3,
title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training},
author = {
Nathan Lambert and
Jacob Morrison and
Valentina Pyatkin and
Shengyi Huang and
Hamish Ivison and
Faeze Brahman and
Lester James V. Miranda and
Alisa Liu and
Nouha Dziri and
Shane Lyu and
Yuling Gu and
Saumya Malik and
Victoria Graf and
Jena D. Hwang and
Jiangjiang Yang and
Ronan Le Bras and
Oyvind Tafjord and
Chris Wilhelm and
Luca Soldaini and
Noah A. Smith and
Yizhong Wang and
Pradeep Dasigi and
Hannaneh Hajishirzi
},
year = {2024},
email = {tulu@allenai.org}
}
```

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