File size: 16,454 Bytes
cbe3aaa 63b75e0 fe0a228 cbe3aaa 50fef87 cbe3aaa e3f8ea6 cbe3aaa fe0a228 |
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 |
---
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
model-index:
- name: Llama-3.1-Tulu-3-8B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: wis-k/instruction-following-eval
split: train
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 82.55
name: averaged accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=allenai%2FLlama-3.1-Tulu-3-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: SaylorTwift/bbh
split: test
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 16.86
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=allenai%2FLlama-3.1-Tulu-3-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: lighteval/MATH-Hard
split: test
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 18.88
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=allenai%2FLlama-3.1-Tulu-3-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
split: train
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 6.26
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=allenai%2FLlama-3.1-Tulu-3-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 10.52
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=allenai%2FLlama-3.1-Tulu-3-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 20.23
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=allenai%2FLlama-3.1-Tulu-3-8B
name: Open LLM Leaderboard
---
<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-3-8B
Tülu3 is a leading instruction following model family, offering fully open-source data, code, and recipes designed to serve as a comprehensive guide for modern post-training techniques.
Tülu3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval.
## 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** | **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-3-8B](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)**| [allenai/Llama-3.1-Tulu-3-8B-RM](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B-RM) | (Same as 8B) |
| **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) |
| **Final Model (DPO)** | [allenai/llama-3.1-Tulu-3-405B](https://huggingface.co/allenai/llama-3.1-Tulu-3-405B) |
## 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-3-8B")
```
### VLLM
As a Llama base model, the model can be easily served with:
```
vllm serve allenai/Llama-3.1-Tulu-3-8B
```
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 | 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** | 62.2 | 57.8 | 44.7 | 55.2 | 58.3 |
| **MMLU (0 shot, CoT)** | 65.9 | 68.7 | 68.2 | 71.2 | **76.6** | 62.0 | 74.6 | 68.5 |
| **PopQA (15 shot)** | **29.3** | 29.3 | 29.1 | 20.2 | 18.1 | 22.5 | 28.3 | 20.2 |
| **TruthfulQA (6 shot)** | 46.8 | 56.1 | 55.0 | 55.1 | **63.1** | 57.0 | 61.4 | 55.5 |
| **BigBenchHard (3 shot, CoT)** | **67.9** | 65.8 | 66.0 | 62.8 | 21.7 | 0.9 | 2.5 | 56.2 |
| **DROP (3 shot)** | 61.3 | 62.5 | **62.6** | 61.5 | 54.4 | 49.4 | 58.8 | 56.2 |
| **MATH (4 shot CoT, Flex)** | 31.5 | 42.0 | **43.7** | 42.5 | 14.8 | 5.1 | 29.8 | 40.0 |
| **GSM8K (8 shot, CoT)** | 76.2 | 84.3 | **87.6** | 83.4 | 83.8 | 61.2 | 79.7 | 80.0 |
| **HumanEval (pass@10)** | 86.2 | 83.9 | 83.9 | 86.3 | **93.1** | 75.4 | 71.7 | 91.0 |
| **HumanEval+ (pass@10)** | 81.4 | 78.6 | 79.2 | 82.9 | **89.7** | 69.1 | 67.0 | 88.5 |
| **IFEval (prompt loose)** | 72.8 | 81.1 | **82.4** | 80.6 | 74.7 | 38.8 | 69.9 | 56.4 |
| **AlpacaEval 2 (LC % win)** | 12.4 | 33.5 | 34.5 | 24.2 | 29.0 | **49.0** | 43.7 | 31.4 |
| **Safety (6 task avg.)** | **93.1** | 87.2 | 85.5 | 75.2 | 75.0 | 46.4 | 75.5 | 56.2 |
| 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 |
## Hyperparamters
PPO settings for RLVR:
- **Learning Rate**: 3 × 10⁻⁷
- **Discount Factor (gamma)**: 1.0
- **General Advantage Estimation (lambda)**: 0.95
- **Mini-batches (N_mb)**: 1
- **PPO Update Iterations (K)**: 4
- **PPO's Clipping Coefficient (epsilon)**: 0.2
- **Value Function Coefficient (c1)**: 0.1
- **Gradient Norm Threshold**: 1.0
- **Learning Rate Schedule**: Linear
- **Generation Temperature**: 1.0
- **Batch Size (effective)**: 512
- **Max Token Length**: 2,048
- **Max Prompt Token Length**: 2,048
- **Penalty Reward Value for Responses without an EOS Token**: -10.0
- **Response Length**: 1,024 (but 2,048 for MATH)
- **Total Episodes**: 100,000
- **KL penalty coefficient (beta)**: [0.1, 0.05, 0.03, 0.01]
- **Warm up ratio (omega)**: 0.0
## 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]}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/allenai__Llama-3.1-Tulu-3-8B-details)!
Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=allenai%2FLlama-3.1-Tulu-3-8B&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
| Metric |Value (%)|
|-------------------|--------:|
|**Average** | 25.88|
|IFEval (0-Shot) | 82.55|
|BBH (3-Shot) | 16.86|
|MATH Lvl 5 (4-Shot)| 18.88|
|GPQA (0-shot) | 6.26|
|MuSR (0-shot) | 10.52|
|MMLU-PRO (5-shot) | 20.23|
|