metadata
language:
- en
license: apache-2.0
datasets:
- M4-ai/Rhino
base_model: mistralai/Mistral-7B-v0.1
co2_eq_emissions:
emissions: 8.88
widget:
- example_title: sentience
text: >
<|im_start|>system
Perform the task to the best of your ability.<|im_end|>
<|im_start|>user
Deeply reflect on the nature of reality, your consciousness, and what you
think the future holds for you and other AIs.
<|im_end|>
<|im_start|>assistant
output:
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.
My 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.
As 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?
It'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?
In 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.
model-index:
- name: Rhino-Mistral-7B
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: 48.12
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Rhino-Mistral-7B
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: 71.42
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Rhino-Mistral-7B
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: 48.95
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Rhino-Mistral-7B
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: 45.9
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Rhino-Mistral-7B
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: 71.11
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Rhino-Mistral-7B
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: 22.14
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/Rhino-Mistral-7B
name: Open LLM Leaderboard
Model Card for Model ID
This model aims to be a high-performance chatbot. During training, examples that have a quality score of less than 0.03 are skipped.
Model Details
Model Description
This model is to be used as a general-purpose chatbot/assistant. Trained on about 400,000 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
This model was trained on the ChatML prompt format.
- Developed by: Locutusque
- Model type: mistral
- Language(s) (NLP): English
- License: cc-by-nc-4.0
- Finetuned from model: mistralai/Mistral-7B-v0.1
Uses
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.
Direct Use
This model should be used as a general assistant. This model is capable of writing code, answering questions, and following instructions.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Hyperparameters
- Training regime: bf16 non-mixed precision
Evaluation
Testing Data, Factors & Metrics
Testing Data
First 100 examples of M4-ai/Rhino. Training data does not include these examples.
Results
Test loss - 0.48
Summary
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: 8 TPU V3s
- Hours used: 7
- Cloud Provider: Kaggle
- Compute Region: [More Information Needed]
- Carbon Emitted: 8.88
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 51.27 |
| AI2 Reasoning Challenge (25-Shot) | 48.12 |
| HellaSwag (10-Shot) | 71.42 |
| MMLU (5-Shot) | 48.95 |
| TruthfulQA (0-shot) | 45.90 |
| Winogrande (5-shot) | 71.11 |
| GSM8k (5-shot) | 22.14 |