---
license: apache-2.0
language:
- en
- ja
tags:
- finetuned
library_name: transformers
pipeline_tag: text-generation
---
<img src="./veteus_logo.svg" width="100%" height="20%" alt=""> 

# Our Models
- [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1)

- [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) 

- [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW)

- [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k)

- [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k)
  
## Model Card for VecTeus-v1.0

The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1

VecTeus has the following changes compared to Mistral-7B-v0.1.
- 128k context window (8k context in v0.1)
- Achieving both high quality Japanese and English generation
- Can be generated NSFW
- Memory ability that does not forget even after long-context generation

This model was created with the help of GPUs from the first LocalAI hackathon.

We would like to take this opportunity to thank

## List of Creation Methods

- Chatvector for multiple models
- Simple linear merging of result models
- Domain and Sentence Enhancement with LORA
- Context expansion

## Instruction format

  Freed from templates. Congratulations

## Example prompts to improve (Japanese)

  - BAD:　あなたは○○として振る舞います
  - GOOD: あなたは○○です

  - BAD: あなたは○○ができます
  - GOOD: あなたは○○をします

## Performing inference

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Local-Novel-LLM-project/Vecteus-v1"
new_tokens = 1024

model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)

system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- "

prompt = input("Enter a prompt: ")
system_prompt += prompt + "\n-------- "
model_inputs = tokenizer([system_prompt], return_tensors="pt").to("cuda")


generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True)
print(tokenizer.batch_decode(generated_ids)[0])
````

## Merge recipe
  
  
  - VT0.1 = Ninjav1 + Original Lora
  - VT0.2 = Ninjav1 128k + Original Lora
  - VT0.2on0.1 = VT0.1 + VT0.2
  
  - VT1 = all VT Series + Lora + Ninja 128k and Normal

## Other points to keep in mind
- The training data may be biased. Be careful with the generated sentences.
- Memory usage may be large for long inferences.
- If possible, we recommend inferring with llamacpp rather than Transformers.