Our Models
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
Ninja adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following:
USER: Hi ASSISTANT: Hello.</s>
USER: Who are you?
ASSISTANT: I am ninja.</s>
Example prompts to improve (Japanese)
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BAD: あなたは○○として振る舞います
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GOOD: あなたは○○です
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BAD: あなたは○○ができます
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GOOD: あなたは○○をします
Performing inference
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Local-Novel-LLM-project/Ninja-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
- WizardLM2 - 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.