Create README.md

README.md

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+---
+license: bigscience-openrail-m
+datasets:
+- trivia_qa
+language:
+- en
+tags:
+- trl
+- transformers
+- rlhf
+---
+
+# starcoderbase-triviaqa
+
+This model is baesed on https://huggingface.co/bigcode/starcoderbase and is fine-tuned on the TriviaQA dataset using reinforcement learning via TRL's `TextEnvironment` (https://github.com/huggingface/trl/pull/424).
+
+
+### Out of Scope Use
+- Replacing human expertise
+
+
+## Bias, Risks, and Limitations
+- Inherits bias, risks, and limitations from the LLaMA model, as described in the [LLaMA Model Card Bias Evaluation](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#quantitative-analysis) and [Ethical Considerations](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#ethical-considerations). 
+- Retains biases present in the Stack Exchange dataset. Per the [latest developer survey for Stack Overflow](https://survey.stackoverflow.co/2022/),
+which constitutes a significant part of the StackExchange data,
+most users who answered the survey identified themselves as [White or European, men, between 25 and 34 years old, and based in the US (with a significant part of responders from India).](https://survey.stackoverflow.co/2022/#developer-profile-demographics)
+- May generate answers that are incorrect or misleading.
+- May copy answers from the training data verbatim.
+- May generate language that is hateful or promotes discrimination ([example](https://huggingface.co/trl-lib/llama-7b-se-rl-peft/discussions/7#64376083369f6f907f5bfe4c)).
+- May generate language that is offensive to direct or indirect users or to people or groups mentioned.
+
+
+### Recommendations
+- Answers should be validated through the use of external sources.
+- Disparities between the data contributors and the direct and indirect users of the technology should inform developers in assessing what constitutes an appropriate use case.
+- Further research is needed to attribute model generations to sources in the training data, especially in cases where the model copies answers from the training data.