Model Card for Cinder Model Name: Cinder
Created by: Joseph Flowers Updated 1-10-24 New round of training, added gguf model 8bit. Model Overview Cinder is an AI chatbot tailored for engaging users in scientific and educational conversations, offering companionship, and sparking imaginative exploration. It is built on the TinyLlama 1.1B parameter model and trained on a unique combination of datasets.
Development Details (Still in development) Model Architecture: TinyLlama 1.1B (based on the 3T checkpoint) https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T Training Datasets: Interactive chats with GPT-4 where I prompt GPT4 to create chats between a User and Cinder and monitor the results. A lot of Please continue, this took around a month. Note: There are also multi character chats with Vector and Cozmo robots characters, a Computer Voice character that is a narrator, as well as other characters.. A subset of Open Orca: https://huggingface.co/datasets/Open-Orca/OpenOrcatr Q&A content generated by GPT-3.5 Turbo by having it read open source encyclopedias and create QA pairs. Shortened version of Samantha by Eric Hartford https://huggingface.co/datasets/cognitivecomputations/samantha-data OpenAssistant: https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25 Updated 1-10-24 Continued training with sorted Orca dataset to around 600mb for STEM related topics, generated around 100mb of STEM q and a with GPT3.5 and GPT4, a chunk of Samantha dataset, Glaive function calling v2, and python code instruction 18k alpaca dataset, around 1GB total. Core Influences: Inspired by the character 'Data' from Star Trek: The Next Generation, Lewis Carroll's writings, and a range of educational resources.
Key Features Inquisitive Scientist: Cinder shows a passion for space and STEM topics. Quirky Educator: It makes complex concepts engaging and accessible. Empathetic Companion: Cinder is designed to demonstrate understanding and emotional depth. Adventurous Spacefarer: Cinder leads imaginative space adventures. Static yet Sophisticated: While Cinder does not learn or adapt with each interaction, its design encompasses a breadth of knowledge and perspectives.
Intended Use Educational Tool: Enhances STEM learning across different age groups. Companion: Provides meaningful and empathetic dialogues. Creative Guide: Facilitates imaginative exploration in scientific contexts.
Ethical Considerations We emphasize ethical AI practices and the privacy of users. Cinder's development includes measures against misuse and ensures respectful, secure interactions.
Limitations Cinder's responses are fixed and do not adapt or learn from individual interactions. The empathetic responses generated are algorithmic and not a substitute for human empathy.
Future Enhancements and Collaboration I am actively seeking feedback, suggestions, or additional datasets to enhance Cinder's capabilities. Future updates may include more interactive educational modules and advanced empathetic response algorithms. I encourage collaboration and contributions to expand Cinder's educational and creative reach. If you have any suggestions or requests please leave them in the newly created discord channel. https://discord.gg/5ebjDrnZ
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 34.03 |
| AI2 Reasoning Challenge (25-Shot) | 34.04 |
| HellaSwag (10-Shot) | 50.40 |
| MMLU (5-Shot) | 25.75 |
| TruthfulQA (0-shot) | 37.57 |
| Winogrande (5-shot) | 56.43 |
| GSM8k (5-shot) | 0.00 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard34.040
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard50.400
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard25.750
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard37.570
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard56.430
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard0.000