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
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.
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
Detailed results can be found here
| 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 |