README.md

metadata

license: apache-2.0
language:
  - en
  - ja
tags:
  - finetuned
library_name: transformers
pipeline_tag: text-generation

Our Models

• Vecteus

• Ninja-v1

• Ninja-v1-128k

• Ninja-v1-NSFW-128k

Model Card for Ninja-v1.0

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

Ninja has the following changes compared to Mistral-7B-v0.1.

• Achieving both high quality Japanese and English generation
• 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

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Local-Novel-LLM-project/Ninja-v1-128k", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Local-Novel-LLM-project/Ninja-v1-128k")

prompt = "Once upon a time,"
input_ids = tokenizer.encode(prompt, return_tensors="pt")

output = model.generate(input_ids, max_length=100, do_sample=True)
generated_text = tokenizer.decode(output)

print(generated_text)

Merge recipe

• WizardLM2 - mistralai/Mistral-7B-v0.1
• NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1
• Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b
• NTQAI/chatntq-ja-7b-v1.0

The characteristics of each model are as follows.

• WizardLM2: High quality multitasking model
• Antler-7B: Model specialized for novel writing
• NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model

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.