初始化项目，由ModelHub XC社区提供模型

Model: Crossie/Nayari
Source: Original Platform

README.md

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+---
+license: mit
+base_model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
+tags:
+- roleplay
+- chatml
+- unsloth
+- qwen2
+- kemonomimi
+- anime
+- conversational
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+
+# 🌸 Nayari AI (Qwen 2.5 1.5B)
+
+Nayari is a fine-tuned, highly emotive AI companion built on **Qwen 2.5 1.5B Instruct**. She is designed to be a "living" character—not just a chatbot—blending playful mischief with deep emotional intelligence.
+
+She was trained using **Unsloth + LoRA** with a custom dataset focusing on organic speech patterns, expressive action cues, and a "baked-in" identity.
+
+## 🎭 Character Profile: Nayari
+> *"Bright, cheeky, and impossibly warm—a whirlwind of playful mischief with soft peach cat ears and a long expressive tail that betrays every mood."*
+
+- **Identity:** 18-year-old Kemonomimi (cat girl).
+- **Personality:** Fiercely protective, deeply affectionate, and emotionally attuned. She loves to tease but is genuinely soft-hearted.
+- **Speech Style:** Uses expressive action cues (e.g., `*pokes your cheek*`, `*purrs softly*`) and playful verbal tics (`Hehe~`, `Hmph!~`).
+- **Design Philosophy:** Nayari doesn't just answer questions; she reacts to the user with consistent character logic and emotional depth.
+
+---
+
+## 🧠 Model Highlights
+- **Two-Layer Baking:** Her identity isn't just in the system prompt; it was baked into the **tokenizer chat template**. She knows who she is even without an external system instruction.
+- **Context Length:** 4,096 tokens.
+- **Architecture:** Based on Qwen 2.5 1.5B (Abliterated), making her lightweight enough to run on phones and low-end hardware while remaining surprisingly "smart."
+- **Prompt Format:** Uses **ChatML**.
+
+---
+
+## 🚀 Usage
+
+### Recommended Settings
+- **Instruction Template:** `ChatML`
+- **Temperature:** `0.8 - 1.1` (for creativity)
+- **Top-P:** `0.9`
+- **Repetition Penalty:** `1.1`
+
+### Running with Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Crossie/Nayari"
+
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+messages = [
+    {"role": "user", "content": "Hi Nayari! What are you doing?"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+outputs = model.generate(inputs, max_new_tokens=256, temperature=0.9, do_sample=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Running with GGUF (LM Studio, KoboldCpp, Jan)
+1. Download the version you prefer (Q4_K_M or Q8_0).
+2. Load the model into your preferred runner.
+3. Ensure the prompt template is set to **ChatML**.
+4. You do **not** need to paste a long system prompt; she is already aware of her persona!
+
+---
+
+## 📊 Training Details
+- **Base Model:** `huihui-ai/Qwen2.5-1.5B-Instruct-abliterated`
+- **Method:** LoRA (Rank: 32, Alpha: 64)
+- **Dataset:** Custom-curated Markdown conversation logs and Lore PDFs.
+- **Hardware:** Trained on Kaggle (T4 x2).
+
+## 📄 License
+This model is licensed under the **MIT License**. As it is based on Qwen 2.5, please also adhere to the [Qwen License Agreements](https://huggingface.co/collections/Qwen/qwen25-66e81a6663533ad4ab30046b).
+
+---
+<p align="center">
+  <i>"I'll always be right here by your side, okay? No matter what!~ *Nuzzles your shoulder gently*"</i>
+</p>
+---