Quantization made by Richard Erkhov.
OLMo-1B-0724-hf - bnb 4bits
- Model creator: https://huggingface.co/allenai/
- Original model: https://huggingface.co/allenai/OLMo-1B-0724-hf/
Original model description:
license: apache-2.0 datasets: - allenai/dolma language: - en
Model Card for OLMo 1B July 2024
OLMo 1B July 2024 is the latest version of the original OLMo 1B model rocking a 4.4 point increase in HellaSwag, among other evaluations improvements, from an improved version of the Dolma dataset and staged training. This version is for direct use with HuggingFace Transformers from v4.40 on.
OLMo is a series of Open Language Models designed to enable the science of language models. The OLMo models are trained on the Dolma dataset. We release all code, checkpoints, logs, and details involved in training these models.
Model Details
The core models released in this batch are the following:
| Size | Training Tokens | Layers | Hidden Size | Attention Heads | Context Length |
|---|---|---|---|---|---|
| OLMo 1B July 2024 | 3.05 Trillion | 16 | 2048 | 16 | 4096 |
| OLMo 7B July 2024 | 2.75 Trillion | 32 | 4096 | 32 | 4096 |
[Coming soon] We are releasing many checkpoints for these models, for every 1000 training steps.
The naming convention is stepXXX-tokensYYYB.
To load a specific model revision with HuggingFace, simply add the argument revision:
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-0724-hf", revision="step1000-tokens4B")
All revisions/branches are listed in the file revisions.txt.
Or, you can access all the revisions for the models via the following code snippet:
from huggingface_hub import list_repo_refs
out = list_repo_refs("allenai/OLMo-1B-0724-hf")
branches = [b.name for b in out.branches]
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: Oct. 2023, with most data from 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
Uses
Inference
Install Transformers. Then proceed as usual with HuggingFace:
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-0724-hf")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-0724-hf")
message = ["Language modeling is "]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> 'Language modeling is the first step to build natural language generation...'
Alternatively, with the pipeline abstraction:
from transformers import pipeline
olmo_pipe = pipeline("text-generation", model="allenai/OLMo-1B-0724-hf")
print(olmo_pipe("Language modeling is "))
>> 'Language modeling is a branch of natural language processing that aims to...'
Or, you can make this slightly faster by quantizing the model, e.g. AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-0724-hf", 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.
Fine-tuning
Model fine-tuning can be done from the final checkpoint (the main revision of this model) or many intermediate checkpoints. Two recipes for tuning are available.
- Fine-tune with the OLMo repository:
torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \
--data.paths=[{path_to_data}/input_ids.npy] \
--data.label_mask_paths=[{path_to_data}/label_mask.npy] \
--load_path={path_to_checkpoint} \
--reset_trainer_state
For more documentation, see the GitHub readme.
- Further fine-tuning support is being developing in AI2's Open Instruct repository. Details are here.
Evaluation
Core model results for the new and original 7B model are found below.
| Task | Llama-7b | Llama2-7b | Falcon-7b | Mpt-7b | OLMo-7B | Llama2-13b | OLMo 7B 0424 |
|---|---|---|---|---|---|---|---|
| arc_c | 44.5 | 48.5 | 47.5 | 46.5 | 48.5 | 52.8 | 42.5 |
| arc_e | 67.9 | 69.5 | 70.4 | 70.5 | 65.4 | 73.7 | 67.2 |
| boolq | 75.4 | 80.2 | 74.6 | 74.2 | 73.4 | 82.2 | 83.7 |
| copa | 91.0 | 86.0 | 86.0 | 85.0 | 90.0 | 90.0 | 86.0 |
| hellaswag | 76.2 | 76.8 | 75.9 | 77.6 | 76.4 | 78.6 | 75.5 |
| openbookqa | 51.2 | 48.4 | 53.0 | 48.6 | 50.4 | 51.8 | 50.0 |
| piqa | 77.2 | 76.7 | 78.5 | 77.3 | 78.4 | 79.0 | 77.5 |
| sciq | 93.9 | 94.5 | 93.9 | 93.7 | 93.8 | 95.5 | 96.7 |
| winogrande | 70.5 | 69.4 | 68.9 | 69.9 | 67.9 | 73.5 | 69.8 |
| truthfulQA (MC2) | 33.9 | 38.5 | 34.0 | 33.0 | 36.0 | 36.8 | 35.8 |
| MMLU (5 shot MC) | 31.5 | 45.0 | 24.0 | 30.8 | 28.3 | 55.5 | 52.0 |
| GSM8k | 10.0 | 12.0 | 4.0 | 4.5 | 8.5 | 25.0 | 29.0 |
| Full average | 60.3 | 62.1 | 59.2 | 59.3 | 59.8 | 66.2 | 63.8 |
And for the 1B model:
| task | random | StableLM 2 1.6b* | Pythia 1B | TinyLlama 1.1B | OLMo 1B | OLMo 1B 0724 (ours) |
|---|---|---|---|---|---|---|
| arc_challenge | 25 | 43.8 | 33.1 | 34.8 | 34.5 | 36.5 |
| arc_easy | 25 | 63.7 | 50.2 | 53.2 | 58.1 | 55.3 |
| boolq | 50 | 76.6 | 61.8 | 64.6 | 60.7 | 67.5 |
| copa | 50 | 84.0 | 72.0 | 78.0 | 79.0 | 83.0 |
| hellaswag | 25 | 68.2 | 44.7 | 58.7 | 62.5 | 66.9 |
| openbookqa | 25 | 45.8 | 37.8 | 43.6 | 46.4 | 46.4 |
| piqa | 50 | 74.0 | 69.1 | 71.1 | 73.7 | 74.9 |
| sciq | 25 | 94.7 | 86.0 | 90.5 | 88.1 | 93.4 |
| winogrande | 50 | 64.9 | 53.3 | 58.9 | 58.9 | 61.4 |
| Average | 36.1 | 68.4 | 56.4 | 61.5 | 62.4 | 65.0 |
*Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging.
Model Details
Data
For training data details, please see the Dolma documentation. This model uses the new 1.7 version with more data sources, better deduplication, and quality filtering. During the annealing phase we use a higher quality subset of Dolma with a linearly decaying learning rate to 0.
Staged training / annealing
In contrast to the first OLMo, we trained OLMo 7B 0424 with a two-stage curriculum:
- In the first stage, we trained the model from scratch on the Dolma 1.7 dataset. We set a cosine learning rate schedule with a warmup of 2500 steps, a peak learning rate of 3e-4, and a cosine decay to 3e-5 after 3T tokens. We cut off this stage after 2T tokens, when the learning rate is still high.
- At this point we switch to the second stage, in which we train on a higher-quality subset of Dolma 1.7 (see below) for another 50B tokens, while linearly decaying the learning rate to 0. Our high-quality subset includes (1) using all available Wikipedia, OpenWebMath and Flan data, (2) removing Dolma CC, CC News, and Megawika, and (3) rebalancing remaining sources to achieve approximately equal proportions of each. See exact token counts and relative proportions of this second stage mix below. Both stages contribute equally to the final performance of the OLMo model. After the first stage, OLMo 7B 0424 already outperforms the older OLMo. The second stage consistently adds 2 to 3 points of performance on top.
Architecture
OLMo 7B architecture with peer models for comparison.
| OLMo 7B | Llama 2 7B | OpenLM 7B | Falcon 7B | PaLM 8B | |
|---|---|---|---|---|---|
| d_model | 4096 | 4096 | 4096 | 4544 | 4096 |
| num heads | 32 | 32 | 32 | 71 | 16 |
| num layers | 32 | 32 | 32 | 32 | 32 |
| MLP ratio | ~8/3 | ~8/3 | ~8/3 | 4 | 4 |
| LayerNorm type | non-parametric LN | RMSNorm | parametric LN | parametric LN | parametric LN |
| pos embeddings | RoPE | RoPE | RoPE | RoPE | RoPE |
| attention variant | full | GQA | full | MQA | MQA |
| biases | none | none | in LN only | in LN only | none |
| block type | sequential | sequential | sequential | parallel | parallel |
| activation | SwiGLU | SwiGLU | SwiGLU | GeLU | SwiGLU |
| sequence length | 2048 | 4096 | 2048 | 2048 | 2048 |
| batch size (instances) | 2160 | 1024 | 2048 | 2304 | 512 |
| batch size (tokens) | ~4M | ~4M | ~4M | ~4M | ~1M |
| weight tying | no | no | no | no | yes |
Hyperparameters
AdamW optimizer parameters are shown below.
| Size | Peak LR | Betas | Epsilon | Weight Decay |
|---|---|---|---|---|
| 1B | 4.0E-4 | (0.9, 0.95) | 1.0E-5 | 0.1 |
| 7B | 3.0E-4 | (0.9, 0.99) | 1.0E-5 | 0.1 |
Optimizer settings comparison with peer models.
| OLMo 7B | Llama 2 7B | OpenLM 7B | Falcon 7B | |
|---|---|---|---|---|
| warmup steps | 5000 | 2000 | 2000 | 1000 |
| peak LR | 3.0E-04 | 3.0E-04 | 3.0E-04 | 6.0E-04 |
| minimum LR | 3.0E-05 | 3.0E-05 | 3.0E-05 | 1.2E-05 |
| weight decay | 0.1 | 0.1 | 0.1 | 0.1 |
| beta1 | 0.9 | 0.9 | 0.9 | 0.99 |
| beta2 | 0.95 | 0.95 | 0.95 | 0.999 |
| epsilon | 1.0E-05 | 1.0E-05 | 1.0E-05 | 1.0E-05 |
| LR schedule | linear | cosine | cosine | cosine |
| gradient clipping | global 1.0 | global 1.0 | global 1.0 | global 1.0 |
| gradient reduce dtype | FP32 | FP32 | FP32 | BF16 |
| optimizer state dtype | FP32 | most likely FP32 | FP32 | FP32 |
Environmental Impact
OLMo 7B variants were either trained on MI250X GPUs at the LUMI supercomputer, or A100-40GB GPUs provided by MosaicML. A summary of the environmental impact. Further details are available in the paper.
| GPU Type | Power Consumption From GPUs | Carbon Intensity (kg CO鈧俥/KWh) | Carbon Emissions (tCO鈧俥q) | |
|---|---|---|---|---|
| OLMo 7B Twin | MI250X (LUMI supercomputer) | 135 MWh | 0* | 0* |
| OLMo 7B | A100-40GB (MosaicML) | 104 MWh | 0.656 | 75.05 |
Bias, Risks, and Limitations
Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content. Such content can also be produced unintentionally, especially in the case of bias, so we recommend users consider the risks of applications of this technology.
Otherwise, many facts from OLMo or any LLM will often not be true, so they should be checked.
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, {nathanl} at allenai dot org.
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