Version:

5B GPU: I64

MAX Model

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 all-mpnet-base-v2/5B:

max-pipelines serve --huggingface-repo-id=sentence-transformers/all-mpnet-base-v2

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/embeddings -H "Content-Type: application/json" -d '{
  "input": "Turn this sentence into embeddings",
  "model": "sentence-transformers/all-mpnet-base-v2",
  "encoding_format": "float"
}'

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

Deploy this model to cloud

All-mpnet-base-v2

The all-mpnet-base-v2 model leverages sentence-transformers technology to map sentences and paragraphs into a 768-dimensional vector space, making it suitable for tasks including clustering and semantic search.

Background

The all-mpnet-base-v2 model was trained on extensive sentence-level datasets using a self-supervised contrastive learning objective. The approach involves a model predicting sentence pairs against randomly sampled alternatives. The training relies on advanced hardware, including TPU technology, supported by Google’s Flax and JAX teams.

Intended Uses

This model is crafted for encoding sentences and short paragraphs into vectors capturing semantic meaning, suited for tasks like information retrieval, clustering, and evaluating sentence similarity. For optimal performance, it truncates text exceeding 384-word pieces.

Training Procedure

Pre-training

Utilizing the pre-trained microsoft/mpnet-base, this model underwent further refinement. Detailed pre-training protocols can be explored in their respective model documentation.

Fine-tuning

For fine-tuning, a contrastive objective compares cosine similarity between sentence pairs, adjusted via cross entropy loss. Training spanned 100k steps with a batch size of 1024 and included optimizations like AdamW optimizer with a 2e-5 learning rate.

Hyperparameters

Training was executed on a TPU v3-8, leveraging large-batch training and sequence limits of 128 tokens.

Training Data

Data amalgamated from various datasets exceeded a billion sentence pairs, selected with defined weighted probabilities as detailed in data_config.json.

Dataset	Paper	Number of Training Tuples
Reddit comments (2015-2018)	paper	726,484,430
S2ORC Citation pairs (Abstracts)	paper	116,288,806
WikiAnswers Duplicate question pairs	paper	77,427,422
...	...	...
Total		1,170,060,424

Evaluation

To explore comprehensive evaluations of this model, one can refer to the Sentence Embeddings Benchmark available online.

Citations:

Reddit comments (2015-2018): PolyAI-LDN
S2ORC: AllenAI
WikiAnswers: OQA

For further technical insights, including source code access, refer to the repository containing the complete training scripts.

Metadata

architectures.0	MPNetForMaskedLM
model_type	mpnet

Version: 5B GPU I64

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

You can quickly deploy all-mpnet-base-v2-5B 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 all-mpnet-base-v2-5B:

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 sentence-transformers/all-mpnet-base-v2

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

EmbeddingMODEL CLASS

MAX Model

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HARDWARE

GPU

QUANTIZATION

I64

ARCHITECTURE

MAX Model

MAX GITHUB

Modular / MAX

MODEL

sentence-transformers

sentence-transformers/all-mpnet-base-v2

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all-mpnet-base-v2-5B

All-mpnet-base-v2

Background

Intended Uses

Training Procedure

Pre-training

Fine-tuning

Hyperparameters

Training Data

Evaluation

Metadata

Learn more

Resources & support for running all-mpnet-base-v2-5B

Resources & support for
running all-mpnet-base-v2-5B