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203 changes: 203 additions & 0 deletions License
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Copyright 2023 - The Arcee team. All rights reserved.

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31 changes: 17 additions & 14 deletions README.md
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## Manifesto

A great rift has emerged between general LLMs and the vector stores that are providing them with contextual information. The unification of these systems is an important step in grounding AI systems in efficient, factual domains, where they are utilized not only for their generality, but for their specificity and uniqueness. To this end, we are excited to open source the Arcee Domain Adapted Language Model (DALM) toolkit for developers to build on top of our Arcee open source Domain Pretrained (DPT) LLMs. We believe that our efforts will help usher in the next phase of language modeling, where organization's deeply tailor AI to operate according to their unique intellectual property and worldview.
A great rift has emerged between general LLMs and the vector stores that are providing them with contextual information. The unification of these systems is an important step in grounding AI systems in efficient, factual domains, where they are utilized not only for their generality, but for their specificity and uniqueness. To this end, we are excited to open source the Arcee Domain Adapted Language Model (DALM) toolkit for developers to build on top of our Arcee open source Domain Pretrained (DPT) LLMs. We believe that our efforts will help as we begin next phase of language modeling, where organizations deeply tailor AI to operate according to their unique intellectual property and worldview.

## Research Progress
## Demo DALMs

Query example DALMs created by the Arcee Team.

[DALM-Patent](https://app.arcee.ai) | [DALM-PubMed](https://app.arcee.ai) | [DALM-SEC](https://app.arcee.ai) | [DALM-Yours](https://app.arcee.ai)
:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:
[![](https://i.imgur.com/XgWn1VI.jpg)](https://app.arcee.ai) | [![](https://i.imgur.com/IY73TcV.jpg)](https://app.arcee.ai) | [![](https://i.imgur.com/Geh28Q8.jpg)](https://app.arcee.ai) | [![](https://i.imgur.com/7KOgcEX.png)](https://app.arcee.ai)

## Research Contents

This repository primarily contains code for fine-tuning a **fully differential** Retrieval Augmented Generation (RAG-end2end) architecture.

Expand All @@ -25,8 +33,6 @@ To perform training and evaluation for both the retriever model and the new rag-
- It's important to highlight that the retriever-only training method employs solely the passages and queries, whereas the rag-e2e training code utilizes all three columns.
- In our experiments, we utilize BAAI/bge-large-en as the retriever and employ meta-llama/Llama-2-7b-hf as the generator. It's important to note that this code is designed to be compatible with any embedding model or autoregressive model available in the Hugging Face model repository at https://huggingface.co/models.



## Clone the repositary
- `git clone https://github.com/arcee-ai/DALM.git`
- `cd DALM`
Expand All @@ -38,6 +44,9 @@ Create your desired virtual environment isntall all necasary librries.
## Training

### Train Retriever Only

Train `BAAI/bge-large-en` retriever with contrastive learning.

```
python dalm/training/retriever_only/train_retriever_only.py
--train_dataset_csv_path ./dalm/datasets/toy_data_train.csv" \
Expand All @@ -50,6 +59,9 @@ python dalm/training/retriever_only/train_retriever_only.py
```

### Train Retriever and Generator Jointly (RAG-e2e)

Train `Llama-2-7b` generator jointly with the retriever model `BAAI/bge-large-en`.

```
python dalm/training/rag_e2e/train_rage2e.py \
--dataset_path "./dalm/datasets/toy_data_train.csv" \
Expand All @@ -65,13 +77,4 @@ python dalm/training/rag_e2e/train_rage2e.py \
### Evaluate the top-k recall of the retriver that trained only with constrstive learning


### Evaluate the top-k recall of the retriever and the exact match of the generator in the RAG-e2e models


## Domain Pretrained Models - DPT (Coming Soon)

![DALM](https://i.imgur.com/rqW405I.png)

* DPT-PubMed-7b
* DPT-Patent-7b
* DPT-SEC-7b
### Evaluate the top-k recall of the retriever and the exact match of the generator in the RAG-e2e models

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