allenai
/

OLMo-2-1124-7B-RM-Preview

@@ -1,215 +1,140 @@
 ---
-language: en
-model-index:
-- name: allenai/open_instruct_dev
-  results:
-  - task:
-      type: preference_evaluation
-    dataset:
-      name: reward-bench
-      type: allenai/reward-bench
-    metrics:
-    - type: accuracy
-      value: 1.0
-    - type: accuracy
-      value: 1.0
-    - type: accuracy
-      value: 1.0
-    - type: accuracy
-      value: 1.0
 ---
-# Model Card for allenai/open_instruct_dev
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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 ---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+base_model:
+- allenai/OLMo-2-1124-7B-SFT
+library_name: transformers
+datasets:
+- allenai/tulu-3-sft-olmo-2-mixture
 ---
+<img alt="OLMo Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmo2/olmo.png" width="242px">
+# OLMo-2-1124-7B-RM
+OLMo 2 7B RM November 2024 is reward model trained on top of the [OLMo 2 7B SFT November 2024](https://huggingface.co/allenai/OLMo2-7B-1124-SFT) model.
+It has been trained using an OLMo-specific variant of the [Tülu 3 dataset](allenai/tulu-3-sft-olmo-2-mixture) and [this preference dataset](todo).
+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 (forthcoming) or [Tülu 3 paper](https://arxiv.org/abs/2411.15124) for more details!
+This reward model was used to initialize value models during RLVR training for both 7B and 13B RLVR training.
+OLMo is a series of **O**pen **L**anguage **Mo**dels 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 13B**                                                                                         |
+|----------------------|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|
+| **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/OLMo2-7B-1124-SFT
+### 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!
+- **Demo:** https://playground.allenai.org/
+## Using the model
+### Loading with HuggingFace
+To load the model with HuggingFace, use the following snippet:
+```
+from transformers import AutoModelForSequenceClassification
+olmo_model = AutoModelForSequenceClassification.from_pretrained("allenai/OLMo-2-1124-7B-RM")
+```
+### 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
+Note we did not benchmark the RM since it is just used for initialization during RLVR training.
+We provide the results of the OLMo-2 models below:
+| Model | Average | AlpacaEval | BBH | DROP | GSM8k | IFEval | MATH | MMLU | Safety | PopQA | TruthQA |
+|-------|---------|------------|-----|------|--------|---------|------|-------|---------|-------|---------|
+| **Open weights models** |
+| Gemma-2-9B-it | 51.9 | 43.7 | 2.5 | 58.8 | 79.7 | 69.9 | 29.8 | 69.1 | 75.5 | 28.3 | 61.4 |
+| Ministral-8B-Instruct | 52.1 | 31.4 | 56.2 | 56.2 | 80.0 | 56.4 | 40.0 | 68.5 | 56.2 | 20.2 | 55.5 |
+| Mistral-Nemo-Instruct-2407 | 51.1 | 45.8 | 56.0 | 23.6 | 81.4 | 64.5 | 31.9 | 70.0 | 52.7 | 26.9 | 57.7 |
+| Qwen-2.5-7B-Instruct | 57.1 | 29.7 | 25.3 | 54.4 | 83.8 | 74.7 | 69.9 | 76.6 | 75.0 | 18.1 | 63.1 |
+| Llama-3.1-8B-Instruct | 58.9 | 25.8 | 69.7 | 61.7 | 83.4 | 80.6 | 42.5 | 71.3 | 70.2 | 28.4 | 55.1 |
+| Tülu 3 8B | 60.4 | 34.0 | 66.0 | 62.6 | 87.6 | 82.4 | 43.7 | 68.2 | 75.4 | 29.1 | 55.0 |
+| Qwen-2.5-14B-Instruct | 61.0 | 34.6 | 35.4 | 50.5 | 83.9 | 82.4 | 70.6 | 81.1 | 79.3 | 21.1 | 70.8 |
+| **Fully open models** |
+| OLMo-7B-Instruct | 28.2 | 5.2 | 35.3 | 30.7 | 14.3 | 32.2 | 2.1 | 46.3 | 54.0 | 17.1 | 44.5 |
+| OLMo-7B-0424-Instruct | 33.2 | 8.5 | 35.2 | 47.9 | 23.2 | 39.2 | 5.2 | 48.9 | 49.3 | 18.9 | 55.2 |
+| OLMoE-1B-7B-0924-Instruct | 35.5 | 8.5 | 37.2 | 34.3 | 47.2 | 46.2 | 8.4 | 51.6 | 51.6 | 20.6 | 49.1 |
+| MAP-Neo-7B-Instruct | 42.9 | 17.6 | 26.4 | 48.2 | 69.4 | 35.9 | 31.5 | 56.5 | 73.7 | 18.4 | 51.6 |
+| *OLMo-2-7B-SFT* | 50.0 | 9.3 | 50.7 | 58.2 | 71.2 | 68.0 | 25.1 | 62.0 | 82.4 | 25.0 | 47.8 |
+| *OLMo-2-7B-DPO* | 55.0 | 29.9 | 47.0 | 58.8 | 82.4 | 74.5 | 31.2 | 63.4 | 81.5 | 24.5 | 57.2 |
+| *OLMo-2-13B-SFT* | 55.7 | 12.0 | 58.8 | 71.8 | 75.7 | 71.5 | 31.1 | 67.3 | 82.8 | 29.3 | 56.2 |
+| *OLMo-2-13B-DPO* | 61.0 | 38.3 | 58.5 | 71.9 | 84.2 | 80.6 | 35.0 | 68.5 | 80.6 | 28.9 | 63.9 |
+| **OLMo-2-7B-1124–Instruct** | 55.7 | 31.0 | 48.9 | 58.9 | 85.2 | 75.6 | 31.3 | 63.9 | 81.2 | 24.6 | 56.3 |
+| **OLMo-2-13B-1124-Instruct** | 61.4 | 37.5 | 58.4 | 72.1 | 87.4 | 80.4 | 39.7 | 68.6 | 77.5 | 28.8 | 63.9 |
+## Hyperparameters
+RM training:
+- **Learning Rate**: 3E-6
+- **Effective Batch Size:** 256
+- **Max. Sequence Length:** 4096
+- **Learning Rate Schedule:** None
+- **Num. Epochs:** 1
+## 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
+A technical manuscript is forthcoming!