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-#model-type: -##如 gpt、phi、llama、chatglm、baichuan 等 -#- gpt - -#domain: -##如 nlp、cv、audio、multi-modal -#- nlp - -#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 +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 --- -### 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 +Tulu 3 banner + +# 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 -#安装ModelScope -pip install modelscope -``` -```python -#SDK模型下载 -from modelscope import snapshot_download -model_dir = snapshot_download('allenai/Llama-3.1-Tulu-3.1-8B') -``` -Git下载 -``` -#Git模型下载 -git clone https://www.modelscope.cn/allenai/Llama-3.1-Tulu-3.1-8B.git +# 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 ``` -

如果您是本模型的贡献者，我们邀请您根据模型贡献文档，及时完善模型卡片内容。

\ No newline at end of file +## 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} +} +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..3bc02e7 --- /dev/null +++ b/config.json @@ -0,0 +1,36 @@ +{ + "_name_or_path": "allenai/Llama-3.1-Tulu-3-8B-DPO", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "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": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" 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