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feat: add warnings in documentation about provider transparency
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samuelrince committed Sep 23, 2024

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4 changes: 4 additions & 0 deletions docs/tutorial/providers/anthropic.md
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# Anthropic

!!! danger "Lack of transparency"

Anthropic does not disclose any information about model architecture and inference infrastrucure. Thus, the environmental impacts are estimated with a very low precision.

This guide focuses on the integration of :seedling: **EcoLogits** with the [Anthropic official python client :octicons-link-external-16:](https://github.com/anthropics/anthropic-sdk-python).

Official links:
4 changes: 4 additions & 0 deletions docs/tutorial/providers/cohere.md
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# Cohere

!!! warning "Lack of transparency"

Majority of models released by Cohere are open-weights, but there is no information on the inference infrastructure. Thus, the environmental impacts are estimated with a lower precision.

This guide focuses on the integration of :seedling: **EcoLogits** with the [Cohere official python client :octicons-link-external-16:](https://github.com/cohere-ai/cohere-python).

Official links:
4 changes: 4 additions & 0 deletions docs/tutorial/providers/google.md
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# Google Gemini

!!! danger "Lack of transparency"

Google does not disclose any information about model architecture and inference infrastrucure. Thus, the environmental impacts are estimated with a very low precision.

This guide focuses on the integration of :seedling: **EcoLogits** with the [Google Gemini official python client :octicons-link-external-16:](https://github.com/google-gemini/generative-ai-python).

Official links:
4 changes: 4 additions & 0 deletions docs/tutorial/providers/huggingface_hub.md
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# Hugging Face Hub

!!! info "Lack of transparency"

All models available on Hugging Face inference endpoints are open-weights, but there is not information on the inference infrastructure. Thus, the environmental impacts are estimated with a lower precision.

This guide focuses on the integration of :seedling: **EcoLogits** with the [Hugging Face Hub official python client :octicons-link-external-16:](https://github.com/huggingface/huggingface_hub).

Official links:
4 changes: 4 additions & 0 deletions docs/tutorial/providers/litellm.md
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# LiteLLM

!!! warning "Lack of transparency"

Depending on the provider, the level of transparency varies. Thus, the environmental impacts can be estimated with a low precision.

This guide focuses on the integration of :seedling: **EcoLogits** with the [LiteLLM official Python client :octicons-link-external-16:](https://github.com/BerriAI/litellm).

Official links:
5 changes: 5 additions & 0 deletions docs/tutorial/providers/mistralai.md
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Mistral AI python client with version `<1.0.0` will no longer be supported by EcoLogits. See official [migration guide from v0 to v1 :octicons-link-external-16:](https://github.com/mistralai/client-python/blob/main/MIGRATION.md).

!!! warning "Lack of transparency"

Some models released by Mistral AI are not open-weights, plus there is no information on the inference infrastructure. Thus, the environmental impacts are estimated with a lower precision.


This guide focuses on the integration of :seedling: **EcoLogits** with the [Mistral AI official python client :octicons-link-external-16:](https://github.com/mistralai/client-python).

Official links:
4 changes: 4 additions & 0 deletions docs/tutorial/providers/openai.md
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# OpenAI

!!! danger "Lack of transparency"

OpenAI does not disclose any information about model architecture and inference infrastrucure. Thus, the environmental impacts are estimated with a very low precision.

This guide focuses on the integration of :seedling: **EcoLogits** with the [OpenAI official python client :octicons-link-external-16:](https://github.com/openai/openai-python).

Official links:

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