transformers safetensors mistral text-generation en license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
library_name: transformers
license: apache-2.0
model-index:
- name: NeuralHyperion-Medium-Preview
  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: 60.67
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/NeuralHyperion-Medium-Preview
      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: 83.67
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/NeuralHyperion-Medium-Preview
      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: 63.73
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/NeuralHyperion-Medium-Preview
      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: 42.93
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/NeuralHyperion-Medium-Preview
      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: 78.53
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/NeuralHyperion-Medium-Preview
      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: 40.49
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Locutusque/NeuralHyperion-Medium-Preview
      name: Open LLM Leaderboard
---
# Model Card for Locutusque/NeuralHyperion-Medium

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/1lL97kzuxqykXGUT6F593.png)

## Model Details
**Model Name**: Locutusque/NeuralHyperion-Medium  
**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
`Locutusque/NeuralHyperion-Medium` is a state-of-the-art language model fine-tuned on the Hyperion dataset and further fine-tuned using DPO on Argilla’s orca DPO pairs 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 `Locutusque/NeuralHyperion-Medium` 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. It is then further fine-tuned using DPO on Argilla’s orca DPO pairs to further improve reasoning.

## Evaluation Results
Coming soon...

## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Locutusque/NeuralHyperion-Medium"
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 Locutusque/NeuralHyperion-Medium 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}
}
```

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__NeuralHyperion-Medium-Preview)

|             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|

