This is a fine-tuned
granite-7b-lab on OpenShift 4.15 documentation using 45212 Q&A pairs.
The resulting corpus is a 45212 Q&A-pairs. The corups was divided into training (42951 Q&A pairs) and eval (2261 Q&A pairs).
The model was trained on 3000 iterations.
1## INSTRUCTIONS
2<your_instructions_here>
3
4## TASK
5<what_you_want_the_model_to_achieve>
6
7## CONTEXT
8<any_new_or_additional_context_for_answering_question>
9
10## QUESTION
11<question_from_user>
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The model was trained with basic instructions to refuse answering questions unrelated to Kubernetes, OpenShift and Kubernetes related topics.
Due to strict instructions during training, the model may refuse to answer valid Kubernetes or OpenShift questions when topics of the context were not present during training.
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The model has not been aligned to human social preferences, so the model might produce problematic output.
The model might also maintain the limitations and constraints that arise from the base model.
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The model undergoes training on synthetic data, leading to the potential inheritance of both advantages and limitations from the underlying data generation methods.
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In the absence of adequate safeguards and RLHF, there exists a risk of malicious utilization of these models for generating disinformation or harmful content. Caution is urged against complete reliance on a specific language model for crucial decisions or impactful information, as preventing these models from fabricating content is not straightforward. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in ungrounded generation scenarios due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain.
