Model Card for M4-ai/hyperion-medium-preview

Model Details

Model Name: M4-ai/hyperion-medium-preview
Base Model: mistralai/Mistral-7B-v0.1
Publisher: M4-ai
Model Type: Question answering, conversational AI, code generation, medical text comprehension, mathematical reasoning, logical reasoning.
Language: Multi-domain, English language.
License: Apache-2.0

Model Description

M4-ai/hyperion-medium-preview is a state-of-the-art language model fine-tuned on the Hyperion dataset for advanced reasoning across scientific domains. This model is designed to handle complex inquiries and instructions, leveraging the diverse and rich information contained in the Hyperion dataset. Its primary use cases include but are not limited to complex question answering, conversational understanding, code generation, medical text comprehension, mathematical reasoning, and logical reasoning.

Intended Use

This model is intended for researchers and practitioners looking for a powerful tool to tackle challenging problems in scientific domains. It can be used in the following scenarios:

• AI-driven tutoring systems for science, medicine, mathematics, and computer science.
• Assistive tools for professionals requiring fast and accurate domain-specific information retrieval.
• Platforms that require conversational AI capabilities with a focus on technical and scientific reasoning.
• Automation in code generation and understanding complex programming context.

Training Data

The M4-ai/hyperion-medium-preview model was fine-tuned on the Hyperion dataset, which amalgamates various datasets rich in diversity and complexity, including programming, medical texts, mathematical problems, and reasoning tasks.

Evaluation Results

Coming soon...

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "M4-ai/hyperion-medium-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# For a text generation task
input_text = "<|im_start|>user\nWhat are the implications of Einstein's theory of relativity in modern physics?<|im_end|>\n<|im_start|>assistant\n"
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate a response
outputs = model.generate(input_ids, max_length=200, num_return_sequences=1, temperature=0.8, top_p=0.95, top_k=40, repetition_penalty=1.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Known Limitations

The diversity of the dataset could lead to inconsistencies in the model's responses due to variations in data formatting and annotation quality.

Licensing Information

This model is released under the Apache-2.0 license.

Citation Information

If you use M4-ai/hyperion-medium-preview in your research, please cite the Hyperion dataset as follows:

@misc{sebastian_gabarain_2024,
  title = {Hyperion-1: Illuminating the Path to Advanced Reasoning with a High-Quality, Multidisciplinary Question Answering Dataset},
  author = {Sebastian Gabarain},
  publisher = {HuggingFace},
  year = {2024},
  url = {https://huggingface.co/datasets/Locutusque/hyperion-v1.0}
}

Quants

• exl2 by bartowski: https://huggingface.co/bartowski/hyperion-medium-preview-exl2

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	61.67
AI2 Reasoning Challenge (25-Shot)	60.67
HellaSwag (10-Shot)	83.67
MMLU (5-Shot)	63.73
TruthfulQA (0-shot)	42.93
Winogrande (5-shot)	78.53
GSM8k (5-shot)	40.49