91 lines
3.4 KiB
Markdown
91 lines
3.4 KiB
Markdown
---
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license: mit
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base_model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
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tags:
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- roleplay
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- chatml
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- unsloth
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- qwen2
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- kemonomimi
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- anime
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- conversational
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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---
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# 🌸 Nayari AI (Qwen 2.5 1.5B)
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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.
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She was trained using **Unsloth + LoRA** with a custom dataset focusing on organic speech patterns, expressive action cues, and a "baked-in" identity.
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## 🎭 Character Profile: Nayari
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> *"Bright, cheeky, and impossibly warm—a whirlwind of playful mischief with soft peach cat ears and a long expressive tail that betrays every mood."*
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- **Identity:** 18-year-old Kemonomimi (cat girl).
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- **Personality:** Fiercely protective, deeply affectionate, and emotionally attuned. She loves to tease but is genuinely soft-hearted.
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- **Speech Style:** Uses expressive action cues (e.g., `*pokes your cheek*`, `*purrs softly*`) and playful verbal tics (`Hehe~`, `Hmph!~`).
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- **Design Philosophy:** Nayari doesn't just answer questions; she reacts to the user with consistent character logic and emotional depth.
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---
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## 🧠 Model Highlights
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- **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.
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- **Context Length:** 4,096 tokens.
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- **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."
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- **Prompt Format:** Uses **ChatML**.
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---
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## 🚀 Usage
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### Recommended Settings
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- **Instruction Template:** `ChatML`
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- **Temperature:** `0.8 - 1.1` (for creativity)
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- **Top-P:** `0.9`
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- **Repetition Penalty:** `1.1`
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### Running with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Crossie/Nayari"
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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messages = [
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{"role": "user", "content": "Hi Nayari! What are you doing?"}
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(inputs, max_new_tokens=256, temperature=0.9, do_sample=True)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Running with GGUF (LM Studio, KoboldCpp, Jan)
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1. Download the version you prefer (Q4_K_M or Q8_0).
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2. Load the model into your preferred runner.
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3. Ensure the prompt template is set to **ChatML**.
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4. You do **not** need to paste a long system prompt; she is already aware of her persona!
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---
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## 📊 Training Details
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- **Base Model:** `huihui-ai/Qwen2.5-1.5B-Instruct-abliterated`
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- **Method:** LoRA (Rank: 32, Alpha: 64)
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- **Dataset:** Custom-curated Markdown conversation logs and Lore PDFs.
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- **Hardware:** Trained on Kaggle (T4 x2).
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## 📄 License
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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).
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---
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<p align="center">
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<i>"I'll always be right here by your side, okay? No matter what!~ *Nuzzles your shoulder gently*"</i>
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</p>
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---
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