File size: 18,018 Bytes
00ef234 46239c2 00ef234 8de36dc f0f5818 8de36dc 00ef234 7cf214a 00ef234 4c929fe 7cf214a 00ef234 6fb28c3 00ef234 2f0b4fd 00ef234 307ea47 00ef234 307ea47 00ef234 8de36dc ee4a050 00ef234 049441d 9726ae9 00ef234 ee4a050 00ef234 049441d 00ef234 ee4a050 00ef234 049441d ee4a050 00ef234 ee4a050 f0f5818 c4874ac f0f5818 11a6310 f0f5818 60d5466 f0f5818 00ef234 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 |
---
license: llama3.1
language:
- en
pipeline_tag: text-generation
datasets:
- allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
base_model:
- allenai/Llama-3.1-Tulu-3-8B-DPO
library_name: transformers
---
<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:
## 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:
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`):
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):
## Reproduction command
```bash
# clone and check out commit
git clone https://github.com/allenai/open-instruct.git
git checkout 3f37c29ddc97d2c108a7658692d2d2c3708ef182
# run my exact command for launching exps
for learning_rate in 5e-7; do
for beta in 0.01; do
for nspp in 16; do
for m in half-m ; do
for kl_estimator in kl3; do
local_rollout_batch_size=8
# `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
```
## 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 = {[email protected]}
}
``` |