Create README.md

README.md

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+---
+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)
+
+## This is a prototype of Vecteus-v1
+
+
+## Model Card for VecTeus-Poet
+
+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.
+- 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([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.