OLMo-0424-7B Model | MAX Builds

Version:

7B GPU:

PyTorch

This version is not quantized and a GPU is recommended.

Install our magic package manager:
```
curl -ssL https://magic.modular.com/ | bash
```
Then run the source command that's printed in your terminal.

Install Max Pipelines in order to run this model.

magic global install max-pipelines && magic global update

Start a local endpoint for OLMo-0424/7B:

max-pipelines serve --huggingface-repo-id=allenai/OLMo-7B-0424

The endpoint is ready when you see the URI printed in your terminal:

Server ready on http://0.0.0.0:8000 (Press CTRL+C to quit)

Now open another terminal to send a request using curl:

curl -N http://0.0.0.0:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
    "model": "allenai/OLMo-7B-0424",
    "stream": true,
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the World Series in 2020?"}
    ]
}' | grep -o '"content":"[^"]*"' | sed 's/"content":"//g' | sed 's/"//g' | tr -d '
' | sed 's/\n//g'

🎉 Hooray! You’re running Generative AI. Our goal is to make this as easy as possible.

Deploy this model to cloud

Model Card for OLMo 7B April 2024

OLMo 7B April 2024 is an advanced version of the original OLMo 7B language model, achieving a 24-point increase in MMLU scores, among other improvements, thanks to enhanced training and dataset upgrades.

Model Details

The core models released in this batch include:

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
OLMo 7B April 2024	2.05 Trillion	32	4096	32	4096

Note: OLMo 7B April 2024 includes QKV clipping.

Model Description

Developed by: Allen Institute for AI (AI2)
Supported by: Databricks, Kempner Institute at Harvard University, AMD, CSC (Lumi Supercomputer), UW
Model type: Transformer-style autoregressive language model.
Language(s): English
License: Apache 2.0
Contact: Technical inquiries: olmo@allenai.org. Press: press@allenai.org
Date cutoff: October 2023

Model Sources

Project Page: OLMo Project Page
Repositories:
Paper: Link
Technical blog post: OLMo Blog Post

Uses

Inference

Quickly get inference running by installing the required packages and using the provided methods for text generation and optional model quantization.

Fine-tuning

Model fine-tuning is supported with scripts and guidelines available through the OLMo repository and AI2's Open Instruct repository.

Evaluation

Evaluation results show significant performance improvements, with detailed results for both the 7B and 1B models across various tasks.

Model Details

Data

The model uses an updated version of the Dolma dataset, featuring improved deduplication and data quality filtering processes.

Staged Training / Annealing

OLMo April 2024 employs a two-stage training curriculum for enhanced model performance, involving initial comprehensive dataset training followed by a high-quality subset tuning.

Architecture

Comparison details of the OLMo 7B architecture with peer models highlight its specifications and capabilities.

Hyperparameters

Optimizer parameters for AdamW are provided, along with a comparison of hyperparameters with peer models.

Bias, Risks, and Limitations

Be aware of potential generation of harmful or biased content and verify facts produced by the model as inaccuracies may occur.

Citation

BibTeX:

@article{Groeneveld2023OLMo,
  title={OLMo: Accelerating the Science of Language Models},
  author={...},
  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., ... & Hajishirzi, H. (2024). OLMo: Accelerating the Science of Language Models. Preprint.

Model Card Contact

For issues with this model card, contact Nathan at {nathanl}@allenai.org.

Metadata

architectures.0	OLMoForCausalLM
model_type	olmo

Version: 7B GPU undefined

This code works on compatible Linux machines.
We are actively working on enabling MAX Serve for MacOS ARM64 as well.

You can quickly deploy OLMo-0424-7B to an endpoint using our MAX container. It includes the latest version of MAX with GPU support and our Python-based inference server called MAX Serve.

With the following Docker command, you’ll get an OpenAI-compatible endpoint running OLMo-0424-7B:

docker run --gpus 1 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_HUB_ENABLE_HF_TRANSFER=1" \
    --env "HF_TOKEN=" \
    -p 8000:8000 \
    docker.modular.com/modular/max-openai-api:nightly \
    --huggingface-repo-id allenai/OLMo-7B-0424

In order to download the model from Hugging Face, you just need to fill in the HF_TOKEN value with your access token, unless the model is from https://huggingface.co/modularai.

Learn more

For more information about the container image, see the MAX container documentation.

To learn more about how to deploy MAX to the cloud, check out our MAX Serve tutorials.

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DETAILS

ChatMODEL CLASS

PyTorch

HARDWARE

GPU

QUANTIZATION

ARCHITECTURE

PyTorch

MAX GITHUB

Modular / MAX

MODEL

allenai

allenai/OLMo-7B-0424

QUESTIONS ABOUT THIS MODEL?

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OLMo-0424-7B

Model Card for OLMo 7B April 2024

Model Details

Model Description

Model Sources

Uses

Inference

Fine-tuning

Evaluation

Model Details

Data

Staged Training / Annealing

Architecture

Hyperparameters

Bias, Risks, and Limitations

Citation

BibTeX:

APA:

Model Card Contact

Metadata

Learn more

Resources & support for running OLMo-0424-7B

Resources & support for
running OLMo-0424-7B