Create README.md

5b6d4e3 verified 17 days ago

6.28 kB

	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	base_model:
	- allenai/OLMo-2-1124-13B-DPO
	library_name: transformers
	datasets:
	- allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
	---

	<img src="https://allenai.org/olmo/olmo-7b-animation.gif" alt="OLMo Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

	# OLMo-2-1124-13B-Instruct

	OLMo-2 13B Instruct November 2024 is finetuned variant of the [OLMo-2 13B November 2024](https://huggingface.co/allenai/OLMo2-13B-1124) model, which has undergone supervised finetuning on the [Tülu 3 dataset](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) and further DPO training on [this dataset](https://huggingface.co/datasets/allenai/olmo-2-1124-13b-preference-mix), and finally RLVR training using [this data](https://huggingface.co/datasets/allenai/RLVR-GSM-MATH-IF-Mixed-Constraints).
	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.
	Check out [the OLMo-2 paper](https://TODO) or [Tülu 3 paper](https://arxiv.org/abs/2411.15124) for more details!

	OLMo is a series of Open Language Models designed to enable the science of language models.
	These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details.
	The core models released in this batch include the following:


	\| Stage \| OLMo-2 7B \| OLMo-2 7B \|
	\|----------------------\|----------------------------------------------------------------------------------------------------------\|----------------------------------------------------------------------------------------------------------\|
	\| Base Model \| [allenai/OLMo2-7B-1124](https://huggingface.co/allenai/OLMo2-7B-1124) \| [allenai/OLMo-2-13B-1124](https://huggingface.co/allenai/OLMo-2-13B-1124) \|
	\| SFT \| [allenai/OLMo-2-1124-7B-SFT](https://huggingface.co/allenai/OLMo-2-1124-7B-SFT) \| [allenai/OLMo-2-1124-13B-SFT](https://huggingface.co/allenai/OLMo-2-1124-13B-SFT) \|
	\| DPO \| [allenai/OLMo-2-1124-7B-DPO](https://huggingface.co/allenai/OLMo-2-1124-7B-DPO) \| [allenai/OLMo-2-1124-13B-DPO](https://huggingface.co/allenai/OLMo-2-1124-13B-DPO) \|
	\| Final Models (RLVR) \| [allenai/OLMo-2-1124-7B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct) \| [allenai/OLMo-2-1124-13B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct) \|
	\| Reward Model (RM)\| [allenai/OLMo-2-1124-7B-RM](https://huggingface.co/allenai/OLMo-2-1124-7B-RM) \| (Same as 8B) \|



	## Model description

	- Model type: A model trained on a mix of publicly available, synthetic and human-created datasets.
	- Language(s) (NLP): Primarily English
	- License: Apache 2.0
	- Finetuned from model: allenai/OLMo-2-13B-1124-DPO

	### Model Sources

	- Project Page: https://allenai.org/olmo
	- Repositories:
	- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
	- Evaluation code: https://github.com/allenai/olmes
	- Further fine-tuning code: https://github.com/allenai/open-instruct
	- Paper: Coming soon! TODO
	- Demo: https://playground.allenai.org/

	## Using the model

	### Loading with HuggingFace

	To load the model with HuggingFace, use the following snippet:
	```
	from transformers import AutoModelForCausalLM

	olmo_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-13B-Instruct")
	```

	### Chat template

	The chat template for our models is formatted as:
	```
	<\|endoftext\|><\|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:
	```
	<\|endoftext\|><\|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 OLMo 2, 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 OLMo-2 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).
	See the Falcon 180B model card for an example of this.


	## Performance

	TODO

	## Hyperparameters

	ppo_olmo_13b_25_rm_best_gsm_math_if_beta_0.03_lr_4e-7_25218__1__1732572469_step_360

	PPO settings for RLVR:
	- Learning Rate: 4 × 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: 2,048
	- Total Episodes: 100,000 (this checkpoint is training step 360)
	- KL penalty coefficient (beta): 0.03
	- Warm up ratio (omega): 0.0

	## License and use

	OLMo-2 is licensed under the Apache 2.0 license.
	OLMo-2 is intended for research and educational use.
	For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).

	## Citation

	If OLMo-2 or any of the related materials were helpful to your work, please cite:
	```
	TODO
	```