Olmo 7B Instruct

🚀 OLMo 7B Instruct

OLMo 7B Instruct is an adapted version of the OLMo series, designed for better question - answering performance. It showcases the potential of OLMo base models with fine - tuning techniques.

🚀 Quick Start

For transformers versions v4.40.0 or newer, we suggest using OLMo 7B Instruct HF instead.

OLMo is a series of Open Language Models aimed at advancing the science of language models. The base models are trained on the Dolma dataset. The adapted versions are trained on the Tulu SFT mixture and, for the Instruct version, a cleaned version of the UltraFeedback dataset. We release all code, checkpoints, logs (coming soon), and details involved in training these models.

OLMo 7B Instruct and OLMo SFT are two adapted versions trained for better question - answering. They demonstrate the performance improvement that OLMo base models can achieve with existing fine - tuning techniques.

Note: This model requires installing ai2 - olmo with pip and using ai2 - olmo>=0.3.0 or HuggingFace Transformers<=4.39. New versions of the model will be released soon with compatibility improvements.

Note that this is the instruct variant of the original OLMo release - there are newer (better) versions of OLMo out now! Check here for newer releases.

✨ Features

• OLMo is an open - source language model series, promoting the research and development of language models.
• The adapted versions, especially OLMo 7B Instruct, show improved question - answering performance through fine - tuning.
• All training - related details are released, facilitating further research and application.

📦 Installation

Quickly get inference running with the following required installation:

pip install ai2-olmo

💻 Usage Examples

Basic Usage

from hf_olmo import OLMoForCausalLM, OLMoTokenizerFast
olmo = OLMoForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct")
tokenizer = OLMoTokenizerFast.from_pretrained("allenai/OLMo-7B-Instruct")
chat = [
    { "role": "user", "content": "What is language modeling?" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(input_ids=inputs.to(olmo.device), max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> '<|user|>\nWhat is language modeling?\n<|assistant|>\nLanguage modeling is a type of natural language processing (NLP) task or machine learning task that...'

Advanced Usage

You can make this slightly faster by quantizing the model, e.g. OLMoForCausalLM.from_pretrained("allenai/OLMo-7B-Instruct", torch_dtype=torch.float16, load_in_8bit=True) (requires bitsandbytes). The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as inputs.input_ids.to('cuda') to avoid potential issues.

Note, you may see the following error if ai2 - olmo is not installed correctly, which is caused by internal Python check naming. We'll update the code soon to make this error clearer.

    raise ImportError(
ImportError: This modeling file requires the following packages that were not found in your environment: hf_olmo. Run `pip install hf_olmo`

📚 Documentation

Model Details

We release two adapted model versions: The base models related to this adapted model are presented in the following tables:

Model Description

• Developed by: Allen Institute for AI (AI2)
• Supported by: Databricks, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, AMD, CSC (Lumi Supercomputer), UW
• Model type: a Transformer style autoregressive language model.
• Language(s) (NLP): English
• License: The code and model are released under Apache 2.0.
• Contact: Technical inquiries: olmo at allenai dot org. Press: press at allenai dot org
• Date cutoff: Feb./March 2023 based on Dolma dataset version.

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/OLMo - Eval
  • Further fine - tuning code: https://github.com/allenai/open - instruct
• Paper: Link
• Technical blog post: https://blog.allenai.org/olmo - open - language - model - 87ccfc95f580
• W&B Logs: https://wandb.ai/ai2 - llm/OLMo - 7B/reports/OLMo - 7B -- Vmlldzo2NzQyMzk5

Uses

This section focuses on the inference usage of the model.

Evaluation

Core model results for the 7B adapted models are as follows:

*Following Ivison et al. 2023, we do not report Tulu 2 TruthfulQA scores due to test set contamination.

Data

For training data details, please see the Dolma, Tulu 2, and UltraFeedback documentation.

Architecture

[No specific content provided in the original, so skipped]

Hyperparameters

The hyperparameters for the two phases of training are as follows:

Compared to Tulu 2, DPO hyperparameters are the same. SFT is lower LR and 3 epochs instead of 2 (and 2k length instead of 8k).

🔧 Technical Details

The model is based on a Transformer - style autoregressive architecture. The training process involves two main phases: SFT and DPO, with specific hyperparameters set for each phase. The base models are trained on the Dolma dataset, and the adapted versions use additional datasets for fine - tuning.

📄 License

The code and model are released under Apache 2.0.

Bias, Risks, and Limitations

This adapted OLMo model is a research artifact. It is intended to benefit the research community interested in understanding the safety properties of LLMs and developers building safety tools for LLMs. For this reason, the model does not include a specific safety filter or safety training data. While our model scores well relative to its peers on ToxiGen, it is possible for the model to generate harmful and sensitive content from some user prompts. We recommend developers exercise caution and consider the risks of the applications of this technology. Furthermore, developers should consider implementing safeguards for biases, privacy, and other potential harms when appropriate. Finally, as with every LLM, OLMo may produce factual - sounding outputs that may not be true, so developers and users are encouraged to confirm such outputs before relying on them. All users of this model are responsible for how they use the model.

Citation

BibTeX:

@article{Groeneveld2023OLMo,
  title={OLMo: Accelerating the Science of Language Models},
  author={Groeneveld, Dirk and Beltagy, Iz and Walsh, Pete and Bhagia, Akshita and Kinney, Rodney and Tafjord, Oyvind and Jha, Ananya Harsh and Ivison, Hamish and Magnusson, Ian and Wang, Yizhong and Arora, Shane and Atkinson, David and Authur, Russell and Chandu, Khyathi and Cohan, Arman and Dumas, Jennifer and Elazar, Yanai and Gu, Yuling and Hessel, Jack and Khot, Tushar and Merrill, William and Morrison, Jacob and Muennighoff, Niklas and Naik, Aakanksha and Nam, Crystal and Peters, Matthew E. and Pyatkin, Valentina and Ravichander, Abhilasha and Schwenk, Dustin and Shah, Saurabh and Smith, Will and Subramani, Nishant and Wortsman, Mitchell and Dasigi, Pradeep and Lambert, Nathan and Richardson, Kyle and Dodge, Jesse and Lo, Kyle and Soldaini, Luca and Smith, Noah A. and Hajishirzi, Hannaneh},
  journal={Preprint},
  year={2024}
}

APA:

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Dodge, J., Lo, K., Soldaini, L., Smith, N., & Hajishirzi, H. (2024). OLMo: Accelerating the Science of Language Models. Preprint.

Model Card Contact

For errors in this model card, contact Nathan or Jacob, {nathanl, jacobm} at allenai dot org.