Update README.md

README.md

@@ -1,215 +1,126 @@
 ---
-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
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-# Model Card for allenai/open_instruct_dev
+<img alt="OLMo Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmo2/olmo.png" width="242px">
 
-<!-- Provide a quick summary of what the model is/does. -->
+OLMo 2 32B Instruct March 2025 is post-trained variant of the [OLMo-2 32B March 2025](https://huggingface.co/allenai/OLMo-2-0325-32B/) model, which has undergone supervised finetuning on an OLMo-specific variant of the [Tülu 3 dataset](https://huggingface.co/datasets/allenai/tulu-3-sft-olmo-2-mixture) and further DPO training on [this dataset](https://huggingface.co/datasets/allenai/olmo-2-1124-7b-preference-mix), and finally RLVR training using [this data](https://huggingface.co/datasets/allenai/RLVR-GSM).
+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://arxiv.org/abs/2501.00656) or [Tülu 3 paper](https://arxiv.org/abs/2411.15124) for more details!
 
+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, and associated training details. 
 
 
-## Model Details
+## Model description
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- **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-2-0325-32B
+
+### Model Sources
+
+- **Project Page:** https://allenai.org/olmo
+- **Repositories:** 
+    - Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo-core
+    - Evaluation code: https://github.com/allenai/olmes
+    - Further fine-tuning code: https://github.com/allenai/open-instruct
+- **Paper:** https://arxiv.org/abs/2501.00656
+- **Demo:** https://playground.allenai.org/
+
+## Installation
+
+OLMo 2 will be supported in the next version of Transformers, and you need to install it from the main branch using:
+```bash
+pip install --upgrade git+https://github.com/huggingface/transformers.git
+```
+
+## 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-2-0325-32B-DPO")
+```
+
+### 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
+
+| 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 | 50.9 | 45.8 | 54.6 | 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 | 60.8 | 34.6 | 34.0 | 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 | 33.1 | 8.5 | 34.4 | 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* | 50.2 | 10.2 | 49.7 | 59.6 | 74.6 | 66.9 | 25.3 | 61.1 | 82.1 | 23.6 | 48.6 |
+| *OLMo-2-7B-DPO* | 54.2 | 27.9 | 46.7 | 60.2 | 82.6 | 73.0 | 30.3 | 60.8 | 81.0 | 23.5 | 56.0 |
+| *OLMo-2-13B-SFT* | 55.3 | 11.5 | 59.6 | 71.3 | 76.3 | 68.6 | 29.5 | 68.0 | 82.3 | 29.4 | 57.1 |
+| *OLMo-2-13B-DPO* | 60.6 | 38.3 | 57.9 | 71.5 | 82.3 | 80.2 | 35.2 | 67.9 | 79.7 | 29.0 | 63.9 |
+| **OLMo-2-7B-1124–Instruct** | 54.8 | 29.1 | 46.6 | 60.5 | 85.1 | 72.3 | 32.5 | 61.3 | 80.6 | 23.2 | 56.5 |
+| **OLMo-2-13B-1124-Instruct** | 62.0 | 39.5 | 58.8 | 71.5 | 87.4 | 82.6 | 39.2 | 68.5 | 79.1 | 28.8 | 64.3 |
+
+
+## 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).
+This model has 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).
+
+## Citation
+
+```bibtex
+@article{olmo20242olmo2furious,
+      title={2 OLMo 2 Furious}, 
+      author={Team OLMo and Pete Walsh and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Shane Arora and Akshita Bhagia and Yuling Gu and Shengyi Huang and Matt Jordan and Nathan Lambert and Dustin Schwenk and Oyvind Tafjord and Taira Anderson and David Atkinson and Faeze Brahman and Christopher Clark and Pradeep Dasigi and Nouha Dziri and Michal Guerquin and Hamish Ivison and Pang Wei Koh and Jiacheng Liu and Saumya Malik and William Merrill and Lester James V. Miranda and Jacob Morrison and Tyler Murray and Crystal Nam and Valentina Pyatkin and Aman Rangapur and Michael Schmitz and Sam Skjonsberg and David Wadden and Christopher Wilhelm and Michael Wilson and Luke Zettlemoyer and Ali Farhadi and Noah A. Smith and Hannaneh Hajishirzi},
+      year={2024},
+      eprint={2501.00656},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.00656}, 
+}
+```