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Model: allenai/tulu-v2.5-ppo-13b-uf-mean-70b-mix-rm Source: Original Platform
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README.md
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---
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model-index:
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- name: tulu-v2.5-ppo-13b-uf-mean-70b-mix-rm
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results: []
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datasets:
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- allenai/tulu-2.5-preference-data
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- allenai/tulu-v2-sft-mixture
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language:
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- en
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base_model: allenai/tulu-2-13b
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license: apache-2.0
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---
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<center>
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<img src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/tulu-2.5/tulu_25_banner.png" alt="Tulu 2.5 banner image" width="800px"/>
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</center>
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# Model Card for Tulu V2.5 PPO 13B - UltraFeedback Mean w. 70B mixture RM
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Tulu is a series of language models that are trained to act as helpful assistants.
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Tulu V2.5 is a series of models trained using DPO and PPO starting from the [Tulu 2 suite](https://huggingface.co/collections/allenai/tulu-v2-suite-6551b56e743e6349aab45101).
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This model is trained using PPO.
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We used a 70B RM trained on our preference data mix, and then used the UltraFeedback prompts during PPO training.
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For more details, read the paper:
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[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://arxiv.org/abs/2406.09279).
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## .Model description
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- **Model type:** One model belonging to a suite of RLHF tuned chat models on a mix of publicly available, synthetic and human-created datasets.
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0.
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- **Finetuned from model:** [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
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### Model Sources
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- **Repository:** https://github.com/allenai/open-instruct
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- **Dataset:** Data used to train this model can be found [here](https://huggingface.co/datasets/allenai/tulu-2.5-preference-data) - specifically the `ultrafeedback_mean_aspects` split. Only the prompts were used.
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- **Model Family:** The collection of related models can be found [here](https://huggingface.co/collections/allenai/tulu-v25-suite-66676520fd578080e126f618).
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- **Reward Model:** The reward model used during PPO training can be found [here](https://huggingface.co/allenai/tulu-v2.5-70b-preference-mix-rm), and the data used to train it [here](https://huggingface.co/datasets/allenai/tulu-2.5-preference-data) - specifically the `preference_big_mixture` split.
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- **Value Model:** The value model produced during PPO training can be found [here](https://huggingface.co/allenai/tulu-v2.5-ppo-13b-uf-mean-70b-mix-rm-value).
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## Input Format
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The model is trained to use the following format (note the newlines):
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```
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<|user|>
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Your message here!
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<|assistant|>
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```
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For best results, format all inputs in this manner. **Make sure to include a newline after `<|assistant|>`, this can affect generation quality quite a bit.**
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We have included a [chat template](https://huggingface.co/docs/transformers/main/en/chat_templating) in the tokenizer implementing this template.
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## Intended uses & limitations
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The model was initially fine-tuned on a filtered and preprocessed of the [Tulu V2 mix dataset](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture), which contains a diverse range of human created instructions and synthetic dialogues generated primarily by other LLMs.
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We then further aligned the model with a [Jax DPO trainer](https://github.com/hamishivi/EasyLM/blob/main/EasyLM/models/llama/llama_train_dpo.py) built on [EasyLM](https://github.com/young-geng/EasyLM) on the dataset mentioned above.
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## Bias, Risks, and Limitations
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The Tulu models have not been aligned to generate safe completions within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
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It is also unknown what the size and composition of the corpus was used to train the base Llama 2 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](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
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### Training hyperparameters
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The following hyperparameters were used during PPO training:
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- learning_rate: 1e-06
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1.0
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- KL penalty coefficient: 0.0325 (we found larger RMs benefited from a lower KL penalty coefficient)
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## Citation
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If you find Tulu 2.5 is useful in your work, please cite it with:
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```
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@misc{ivison2024unpacking,
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title={{Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback}},
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author={{Hamish Ivison and Yizhong Wang and Jiacheng Liu and Ellen Wu and Valentina Pyatkin and Nathan Lambert and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi}}
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year={2024},
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eprint={2406.09279},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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||||
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||||
}
|
||||
1
special_tokens_map.json
Normal file
1
special_tokens_map.json
Normal file
@@ -0,0 +1 @@
|
||||
{"bos_token": {"content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"content": "</s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "unk_token": {"content": "<unk>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}}
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
34
tokenizer_config.json
Normal file
34
tokenizer_config.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
|
||||
}
|
||||
Reference in New Issue
Block a user