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https://github.com/infosys/infosys-transformer-foundry
A LLM Ops platform that hosts pre curated set of open source models that can be fine tunned and deployed for our customers. Allows users to create their own data pipelines with DAG support and provides effective and optimized way to finetune and deploy models.
https://github.com/infosys/infosys-transformer-foundry
Last synced: about 23 hours ago
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A LLM Ops platform that hosts pre curated set of open source models that can be fine tunned and deployed for our customers. Allows users to create their own data pipelines with DAG support and provides effective and optimized way to finetune and deploy models.
- Host: GitHub
- URL: https://github.com/infosys/infosys-transformer-foundry
- Owner: Infosys
- License: apache-2.0
- Created: 2024-04-15T13:56:35.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-04-26T15:03:12.000Z (7 months ago)
- Last Synced: 2024-04-27T14:40:49.706Z (7 months ago)
- Language: Python
- Size: 2.09 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Infosys Transformer Foundry
## Overview
Infosys Transformer Foundry solution provides buildings blocks for managing LLM Ops and model life cycle management such as model selection, finetuning, benchmarking, deployment at scale along with data pipelines.
## Features v1.0
- Model Zoo: Curated list of open source models along with their metadata, lifecycle status and model tagging.
- Leaderboard: LLM leaderboard for customer data (text, embedding and code) on public/private models for efficient selection of models.
- Benchmark tool: Allows benchmarking of fine-tuned or open source models.
## Roadmap
- Data Pipelines: Allow users to create custom data processing workflows for their models.
- Fine Tuning: User can fine tune a model against custom datasets for tailored results.
- Model Deployment: Facilitate deployment of curated or fine-tuned models and create access points.
## Hardware & Software Requirements
Find the hardware and software requirements [here](docs/requirements.md)
## Installation
### Docker
a. Clone the GitHub repository
```
git clone -b < branch and Repo url>
```
b. Build docker container for each component
Navigate to each component folder within the repository and build the corresponding Docker image using the following command:
```dos
cd
docker build -t ${DOCKER_REPO}/
```
c. Run the Docker Container
```dos
docker-compose -f docker-compose.yaml up
```
## Usage and Examples
[Benchmark Evaluation](docs/benchmark_evaluation.md)
## Reporting problems, asking questions
We appreciate your feedbacks, questions or bug reporting regarding this project. When posting issues in GitHub, ensure the posted examples follow the guidelines below:
Minimal: Provide the smallest possible code snippet that still reproduces the problem.
Complete: Include all necessary information (code, configuration, etc.) for someone else to replicate the issue.
Reproducible: Test your provided code to confirm it consistently reproduces the problem.