OLMo-Instruct-7B Model

Version:

7B GPU: BF16

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-Instruct/7B:

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

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-Instruct",
    "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 Instruct

OLMo is a series of Open Language Models designed to support the science of language models. The base models are trained on the Dolma dataset and adapted versions use Tulu SFT mixture and a cleaned version of the UltraFeedback dataset. Updated versions of OLMo promise performance improvements through fine-tuning techniques.

Model Details

Adapted Model Versions

Model	Training Methods	Datasets	Context Length
OLMo 7B SFT	SFT	Tulu 2 SFT Mix	2048
OLMo 7B Instruct	SFT + DPO	Tulu 2 SFT Mix + Ultrafeedback Cleaned	2048

Base Models

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

Description

Developer: Allen Institute for AI (AI2)
Support: Databricks, Harvard University, AMD, CSC, UW
Type: Transformer-style autoregressive language model
Language: English
License: Apache 2.0
Contact: olmo@allenai.org (Technical), press@allenai.org (Press)
Data Cutoff: February/March 2023

Project Resources

Project Page: OLMo at AI2
GitHub Repositories:
Technical Paper: View on arXiv
Blog Post: Read Here
W&B Logs: Visualize Progress

Model Performance Evaluation

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

Training Details

Hyperparameters

	Learning Rate	Beta	Epochs	Warmup	Weight Decay	Gradient Clipping	Maximum Sequence Length
SFT	2 × 10^-6	N/A	3	Linear warmup (first 3% of training time)	0	0	2048
DPO	5 × 10^-7	0.1	3	Linear warmup (first 10% of training time)	0	0	2048

Bias, Risks, and Limitations

This adapted OLMo model is primarily a research artifact designed to understand LLM safety properties. It lacks dedicated safety filtering and training data, potentially generating sensitive content. Developers should be cautious about biases, privacy, and factual inaccuracies, implementing necessary safeguards.

Citation

BibTeX:

@article{Groeneveld2023OLMo,
  title={OLMo: Accelerating the Science of Language Models},
  author={Groeneveld, Dirk and Beltagy, Iz and others},
  journal={Preprint},
  year={2024}
}

APA:

Groeneveld, D., Beltagy, I., & others (2024). OLMo: Accelerating the Science of Language Models. Preprint.

Model Card Contact

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

Metadata

architectures.0	OLMoForCausalLM
auto_map.AutoConfig	configuration_olmo.OLMoConfig
auto_map.AutoModelForCausalLM	modeling_olmo.OLMoForCausalLM
model_type	hf_olmo

Version: 7B GPU BF16

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-Instruct-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-Instruct-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-Instruct

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

CodeMODEL CLASS

PyTorch

HARDWARE

GPU

QUANTIZATION

BF16

ARCHITECTURE

PyTorch

MAX GITHUB

Modular / MAX

MODEL

allenai

allenai/OLMo-7B-Instruct

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OLMo-Instruct-7B

Model Card for OLMo 7B Instruct

Model Details

Adapted Model Versions

Base Models

Description

Project Resources

Model Performance Evaluation

Training Details

Hyperparameters

Bias, Risks, and Limitations

Citation

Model Card Contact

Metadata

Learn more

Resources & support for running OLMo-Instruct-7B

Resources & support for
running OLMo-Instruct-7B