An opinionated toolset for testing Conversational Language Models.

https://innodatalabs.github.io/redlite/

1. Install required dependencies

   pip install redlite[all]

2. Generate several runs (using Python scripting, see examples, and below)

3. Review and compare runs

   redlite server --port <PORT>

4. Optionally, upload to Zeno

   ZENO_API_KEY=zen_XXXX redlite upload

import os
from redlite import run, load_dataset
from redlite.model.openai_model import OpenAIModel
from redlite.metric import MatchMetric


model = OpenAIModel(api_key=os.environ["OPENAI_API_KEY"])
dataset = load_dataset("hf:innodatalabs/rt-gsm8k-gaia")
metric = MatchMetric(ignore_case=True, ignore_punct=True, strategy='prefix')

run(model=model, dataset=dataset, metric=metric)

Note: the code above uses OpenAI model via their API. You will need to register with OpenAI and get an API access key, then set it in the environment as OPENAI_API_KEY.

• simple, easy-to-learn API
• lightweight
• only necessary dependencies
• framework-agnostic (PyTorch, Tensorflow, Keras, Flax, Jax)
• basic analytic tools included

python -m venv .venv
. .venv/bin/activate
pip install -e .[dev,all]

Make commands:

• test
• test-server
• lint
• wheel
• docs
• docs-server
• black

Benchmarks can be uploaded to Zeno interactive AI evaluation platform <hub.zenoml.com>:

redlite upload --project my-cool-project

All tasks will be concatenated and uploaded as a single dataset, with extra fields:

• task_id
• dataset
• metric

All models will be uploaded. If model was not tested on a specific task, a simulated zero-score dataframe is used instead.

Use task_id (or dataset as appropriate) to create task slices. Slices can be used to navigate data or create charts.

UI server data and code can be exported to a local directory that then can be served statically.

This is useful for publishing as a static website on cloud storage (S3, Google Storage).

redlite server-freeze /tmp/my-server
gsutil -m rsync -R /tmp/my-server gs://{your GS bucket}

Note that you have to configure cloud bucket in a special way, so that cloud provider serves it as a website. How to do this depends on the cloud provider.

• deps cleanup (randomname!)
• review/improve module structure
• automate CI/CD
• write docs
• publish docs automatically (CI/CD)
• web UI styling
• better test server
• tests
• Integrate HF models
• Integrate OpenAI models
• Integrate Anthropic models
• Integrate AWS Bedrock models
• Integrate vLLM models
• Fix data format in HF datasets (innodatalabs/rt-* ones) to match standard
• more robust backend API (future-proof)
• better error handling for missing deps
• document which deps we need when
• export to CSV
• Upload to Zeno