初始化项目，由ModelHub XC社区提供模型

Model: Local-Novel-LLM-project/Ninja-v1
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- ja
+tags:
+- finetuned
+library_name: transformers
+pipeline_tag: text-generation
+---
+<img src="./ninjalogo.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 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)
+
+  - 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/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.

config.json

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+{
+  "_name_or_path": "NovelHacja/Wizardntq-ja-7B",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 4096,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.39.3",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.39.3"
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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