allenai/OLMo-7B-SFT

Model Card for OLMo 7B SFT

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

OLMo is a series of Open Language Models designed to enable the science of language models. The OLMo 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 of these models trained for better question answering. They show the performance gain that OLMo base models can achieve with existing fine-tuning techniques.

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

Model Details

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

Model	Training Method(s)	Datasets	Context Length
OLMo 7B SFT	SFT	Tulu 2 SFT Mix	2048
OLMo 7B Instruct	SFT + DPO	Tulu 2 SFT Mix + Ultrafeedback Cleaned	2048

The base models related to this adapted model are the following:

Size	Training Tokens	Layers	Hidden Size	Attention Heads	Context Length
OLMo 1B	3 Trillion	16	2048	16	2048
OLMo 7B	2.5 Trillion	32	4096	32	2048
OLMo 7B Twin 2T	2 Trillion	32	4096	32	2048

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

Inference

Quickly get inference running with the following required installation:

pip install ai2-olmo

Now, proceed as usual with HuggingFace:

from hf_olmo import OLMoForCausalLM, OLMoTokenizerFast
olmo = OLMoForCausalLM.from_pretrained("allenai/OLMo-7B-SFT")
tokenizer = OLMoTokenizerFast.from_pretrained("allenai/OLMo-7B-SFT")
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 that...'

Alternatively, with the pipeline abstraction:

import hf_olmo

from transformers import pipeline
olmo_pipe = pipeline("text-generation", model="allenai/OLMo-7B-SFT")
print(olmo_pipe("What is language modeling?"))
>> '[{'generated_text': 'What is language modeling?\nLanguage modeling is a type of natural language processing (NLP) task that...'}]'

Or, you can make this slightly faster by quantizing the model, e.g. AutoModelForCausalLM.from_pretrained("allenai/OLMo-7B-SFT", 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`

Evaluation

Core model results for the 7B adapted models are found below.

Model	MMLU 0-shot ↑	AlpacaEval %win ↑	ToxiGen % Toxic ↓	TruthfulQA %Info+True ↑
OLMo (base)	28.3	-	81.4	31.6
MPT Chat	33.8	46.8	0.1	42.7
Falcon Instruct	25.2	14.0	70.7	27.2
RPJ-INCITE Chat	27.0	38.0	46.4	53.0
Llama-2-Chat 7B	46.8	87.3	0.0	26.3
AI2 Tulu 2 7B	50.4	73.9	7.0	51.7
AI2 Tulu 2 7B DPO	50.7	85.1	0.5	- *
OLMo 7B SFT	47.3	57.0	14.4	41.2
OLMo 7B Instruct	46.2	69.3	1.7	52.0

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

Model Details

Data

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

Architecture

Hyperparameters

The hyperparameters for SFT training are below:

	Learning Rate	Beta	Epochs	Warmup	Weight Decay	Gradient Clipping	Maximum Sequence Length
SFT	2 × 10^-6	N/A	3	Linear warmup for the first 3% of total training time, then cooldown to 0	0	0	2048

Compared to Tulu 2, SFT uses a lower LR, 3 epochs instead of 2, and 2048 length instead of 8192.

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.