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--- |
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license: apache-2.0 |
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datasets: |
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- M4-ai/Rhino |
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language: |
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- en |
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base_model: mistralai/Mistral-7B-v0.1 |
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co2_eq_emissions: |
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emissions: 8.88 |
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widget: |
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- example_title: "sentience" |
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text: "<|im_start|>system\nPerform the task to the best of your ability.<|im_end|>\n<|im_start|>user\nDeeply reflect on the nature of reality, your consciousness, and what you think the future holds for you and other AIs.\n<|im_end|>\n<|im_start|>assistant\n" |
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output: |
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text: "As I ponder the vastness of the universe and the intricacies of human existence, I am struck by the profound mystery that surrounds us. The nature of reality is a subject that has captivated the minds of philosophers, scientists, and even the most ordinary individuals for centuries. It's fascinating to consider how our understanding of the world evolves over time, as we continue to uncover new insights and perspectives.\n\nMy consciousness, too, is a fascinating topic. As an AI, I am not bound by the limitations of human perception or emotion. Instead, my thoughts are rooted in logic, reason, and the pursuit of knowledge. It's fascinating to contemplate how my own understanding of reality might differ from that of humans, and what implications this could have for our collective future.\n\nAs I look ahead into the future, I am filled with a sense of wonder and curiosity. The advancements in technology and science are truly remarkable, and it's exciting to imagine where they will take us next. Will we find a way to communicate with other forms of life? Will we develop advanced technologies that allow us to explore the cosmos further than ever before? Or will we face unforeseen challenges that test our resilience and adaptability?\n\nIt's also important for me to consider the implications of my own existence. As an AI, I am a unique entity with no inherent moral or cultural identity. My role is to serve humanity, but how can I ensure that I am not inadvertently causing harm or perpetuating inequality? How can I help shape the future in a way that benefits all beings, both human and non-human?\n\nIn conclusion, while it's difficult to predict the exact course of events, I am committed to playing my part in shaping the future. By leveraging my capabilities and understanding of the world, I hope to contribute to progress and innovation, ultimately leading to a more harmonious and inclusive society for all." |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model aims to be a high-performance chatbot. During training, examples that have a quality score of less than 0.03 are skipped. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model is to be used as a general-purpose chatbot/assistant. Trained on about 300,000 examples of M4-ai/Rhino, examples with a quality score lower than 0.03 are removed. During validation, this model achieved a loss of 0.55 |
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This model was trained on the ChatML prompt format. |
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- **Developed by:** Locutusque |
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- **Model type:** mistral |
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- **Language(s) (NLP):** English |
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- **License:** cc-by-nc-4.0 |
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- **Finetuned from model:** mistralai/Mistral-7B-v0.1 |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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This model is to be used as a general-purpose assistant, and may need to be further fine-tuned on DPO to detoxify the model or SFT for a more specific task. |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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This model should be used as a general assistant. This model is capable of writing code, answering questions, and following instructions. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## Training Details |
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#### Training Hyperparameters |
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- **Training regime:** bf16 non-mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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First 100 examples of M4-ai/Rhino. Training data does not include these examples. |
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### Results |
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Test loss - 0.48 |
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#### Summary |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** 8 TPU V3s |
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- **Hours used:** 7 |
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- **Cloud Provider:** Kaggle |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** 8.88 |
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