license: mit
widget:
- text: |
<|system|>
You are a helpful assistant</s>
<|user|>
Tell me about yourself, what is your name?.</s>
<|assistant|>
model-index:
- name: TinyLlama-3T-Cinder-v1.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 34.04
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/TinyLlama-3T-Cinder-v1.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 50.4
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/TinyLlama-3T-Cinder-v1.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.75
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/TinyLlama-3T-Cinder-v1.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 37.57
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/TinyLlama-3T-Cinder-v1.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 56.43
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/TinyLlama-3T-Cinder-v1.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Josephgflowers/TinyLlama-3T-Cinder-v1.1
name: Open LLM Leaderboard
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 |