---
license: apache-2.0
library_name: transformers
datasets:
- NeuralNovel/Neural-Story-v1
- NeuralNovel/Creative-Logic-v1
base_model: mistralai/Mistral-7B-Instruct-v0.2
inference: false
model-index:
- name: Tanuki-7B-v0.1
  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: 62.8
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Tanuki-7B-v0.1
      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: 83.14
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Tanuki-7B-v0.1
      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: 60.54
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Tanuki-7B-v0.1
      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: 66.33
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Tanuki-7B-v0.1
      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: 75.85
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Tanuki-7B-v0.1
      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: 39.8
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=NeuralNovel/Tanuki-7B-v0.1
      name: Open LLM Leaderboard
---
![Neural-Story](https://i.ibb.co/FbBrb5H/OIG-4-Ffmd-Jvny-ZJvn.jpg)

# NeuralNovel/Tanuki-7B-v0.1

Designed to generate instructive and narrative text, with a specific focus on roleplay & short storytelling.
This fine-tune has been tailored to provide detailed and creative responses in the context of complex narrative.

Full-parameter fine-tune (FFT) of Mistral-7B-Instruct-v0.2, with apache-2.0 license, suitable for commercial or non-commercial use.

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### Data-set
The model was finetuned using the Neural-Story-v1 and Creative-Logic-v1 datasets.


### Summary

Fine-tuned with the intention of generating creative and narrative text, making it more suitable for creative writing prompts and storytelling.

#### Out-of-Scope Use

The model may not perform well in scenarios unrelated to instructive and narrative text generation. Misuse or applications outside its designed scope may result in suboptimal outcomes.

### Bias, Risks, and Limitations

The model may exhibit biases or limitations inherent in the training data. It is essential to consider these factors when deploying the model to avoid unintended consequences.

This model and its datasets serves as an excellent starting point for testing language models, users are advised to exercise caution, as there might be some inherent genre or writing bias.

### Hardware and Training

Trained using NVIDIA Tesla T40 24 GB. 

```

 n_epochs = 4, # increased from 3
 n_checkpoints = 2,
 batch_size = 6, # decreased from 20
 learning_rate = 1e-5,


```

*Sincere appreciation to Techmind for their generous sponsorship.*

# [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_NeuralNovel__Tanuki-7B-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |64.74|
|AI2 Reasoning Challenge (25-Shot)|62.80|
|HellaSwag (10-Shot)              |83.14|
|MMLU (5-Shot)                    |60.54|
|TruthfulQA (0-shot)              |66.33|
|Winogrande (5-shot)              |75.85|
|GSM8k (5-shot)                   |39.80|