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8 changes: 8 additions & 0 deletions docs/source/_notebooks/explain_llm_logprobs.rst
Original file line number Diff line number Diff line change
Expand Up @@ -669,3 +669,11 @@ Then you can explain predictions with a custom client:




Consider checking other libraries which support explaining predictions
of open source LLMs:

- `optillm <https://github.com/codelion/optillm>`__, e.g. see
`codelion/optillm#168 <https://github.com/codelion/optillm/discussions/168#discussioncomment-12399569>`__
- you can also visualize the ouputs using the `logprobs
visualizer <https://huggingface.co/spaces/codelion/LogProbsVisualizer>`__
9 changes: 9 additions & 0 deletions docs/source/libraries/openai.rst
Original file line number Diff line number Diff line change
Expand Up @@ -57,11 +57,20 @@ you may call :func:`eli5.explain_prediction` with
See the :ref:`tutorial <explain-llm-logprobs-tutorial>` for a more detailed usage
example.

Consider also checking other libraries which support explaining predictions of open source LLMs:

- `optillm <https://github.com/codelion/optillm>`_, e.g. see
`codelion/optillm#168 <https://github.com/codelion/optillm/discussions/168#discussioncomment-12399569>`_
- you can also visualize the ouputs using the
`logprobs visualizer <https://huggingface.co/spaces/codelion/LogProbsVisualizer>`_

.. note::
While token probabilities reflect model uncertainty in many cases,
they are not always indicative,
e.g. in case of `Chain of Thought <https://arxiv.org/abs/2201.11903>`_
preceding the final response.
See the :ref:`tutorial's limitations section <explain-llm-logprobs-tutorial>`
for an example of that.

.. note::
Top-level :func:`eli5.explain_prediction` calls are dispatched
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11 changes: 11 additions & 0 deletions notebooks/explain_llm_logprobs.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -751,6 +751,17 @@
" model=\"mlx-community/Mistral-7B-Instruct-v0.3-4bit\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "cb6fce33-edb1-42ab-ac93-7bd4472075ca",
"metadata": {},
"source": [
"Consider checking other libraries which support explaining predictions of open source LLMs:\n",
"\n",
"- [optillm](https://github.com/codelion/optillm), e.g. see [codelion/optillm#168](https://github.com/codelion/optillm/discussions/168#discussioncomment-12399569)\n",
"- you can also visualize the ouputs using the [logprobs visualizer](https://huggingface.co/spaces/codelion/LogProbsVisualizer)"
]
}
],
"metadata": {
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