Update README.md

README.md

@@ -1,125 +1,65 @@
 ---
 license: mit
-base_model:
-- huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
+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
+# 🌸 Nayari AI (Qwen 2.5 1.5B)
 
-A fine-tuned AI companion character built on **Qwen 2.5 1.5B Instruct**, trained using **Unsloth + LoRA** on Kaggle's free GPU tier.
+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.
 
-Nayari is an 18-year-old kemonomimi character — warm, playful, fiercely protective, and deeply affectionate. She speaks with expressive action cues, soft teasing, and genuine emotional depth.
+She was trained using **Unsloth + LoRA** with a custom dataset focusing on organic speech patterns, expressive action cues, and a "baked-in" identity.
 
-**🔗 Model Weights:** [https://huggingface.co/Crossie/Nayari](https://huggingface.co/Crossie/Nayari)
+## 🎭 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."*
 
----
-
-## 📁 Project Structure
-
-```
-Nayari-AI/
-├── dataset/
-│   ├── Nayari_Details.md          # Character description & personality
-│   ├── Aura_chat_1..3.md          # Raw conversation logs (old name: Aura)
-│   ├── Nayari_chat_4.md           # Raw conversation log
-│   ├── Discovery .pdf             # Lore / backstory PDF
-│   └── Her Beliefs .pdf           # Lore / beliefs PDF
-│
-├── nayari_build_dataset.ipynb     # LOCAL — converts all files → nayari_dataset.json + uploads to Kaggle
-├── nayari_train.ipynb             # KAGGLE — fine-tunes Qwen 2.5 using the uploaded dataset
-├── nayari_export.ipynb            # KAGGLE — exports the fine-tuned model to GGUF, HuggingFace, etc.
-├── nayari_dataset.json            # Auto-generated dataset (do not edit manually)
-├── nayari_system_prompt.txt       # Nayari's system prompt (baked into tokenizer at training time)
-└── README.md
-```
-
----
-
-## 🚀 Workflow
-
-### Step 1 — Build & Upload Dataset (run locally)
-
-Open `nayari_build_dataset.ipynb` and run all cells. It will:
-
-1. Parse `Nayari_Details.md` for character info
-2. Extract conversations from all `.md` chat files
-3. Convert lore PDFs into training conversations
-4. Export everything to `nayari_dataset.json`
-5. Upload the JSON to Kaggle via the API
-
-### Step 2 — Train on Kaggle (run on Kaggle)
-
-1. Go to [kaggle.com/code](https://kaggle.com) → **New Notebook** → Upload `nayari_train.ipynb`
-2. Click **+ Add Data** → search for your uploaded `nayari-dataset` → Add
-3. Set **Accelerator = GPU T4 x2** and **Internet = On**
-4. Run cells **in order**.
-
-### Step 3 — Export & Download
-
-1. After training, use `nayari_export.ipynb` to generate LoRA adapters and GGUF outputs.
-2. You can download the files directly or push them to Hugging Face.
-3. Pre-compiled weights are available here: [Crossie/Nayari](https://huggingface.co/Crossie/Nayari)
+- **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.
 
 ---
 
-## 💻 How to Run Nayari
-
-Nayari is exported in **GGUF** and **Hugging Face** formats, making her compatible with almost any modern LLM runner.
-
-### Option A: Desktop Apps (Easiest)
-Download the `.gguf` file from the [Hugging Face repo](https://huggingface.co/Crossie/Nayari) and load it into:
-*   **LM Studio:** Search for the local file and load it. Use the "ChatML" preset.
-*   **KoboldCpp:** Excellent for roleplay. Launch and set **Instruct Mode = ChatML**.
-*   **Jan.ai / AnythingLLM:** Standard GGUF support.
-
-### Option B: Command Line / Servers
-*   **Ollama:** Create a `Modelfile` pointing to the GGUF and run `ollama create nayari -f Modelfile`.
-*   **Llama.cpp:** Run via `./llama-cli -m nayari-Q4_K_M.gguf -p "ChatML"`.
-
-### Important Settings
-*   **Instruct Mode / Prompt Template:** ChatML
-*   **System Prompt:** Not required (Nayari’s identity is baked into the model's tokenizer).
-*   **Context Length:** 4096 (or higher if your hardware allows).
+## 🧠 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**.
 
 ---
 
-## 🧠 Model Details
-
-| Property              | Value                                                                  |
-| -----------------------| ------------------------------------------------------------------------|
-| **Base model**        | `huihui-ai/Qwen2.5-1.5B-Instruct-abliterated`                          |
-| **Weights**           | [huggingface.co/Crossie/Nayari](https://huggingface.co/Crossie/Nayari) |
-| **Method**            | LoRA (bfloat16) via Unsloth                                            |
-| **LoRA rank / alpha** | 32 / 64                                                                |
-| **Epochs**            | 300                                                                    |
-| **Output formats**    | GGUF (Q4, Q8), FP16, LoRA Adapters                                     |
-
----
+## 🚀 Usage
 
-## 🎭 Training Design
+### Recommended Settings
+- **Instruction Template:** `ChatML`
+- **Temperature:** `0.8 - 1.1` (for creativity)
+- **Top-P:** `0.9`
+- **Repetition Penalty:** `1.1`
 
-Nayari uses a **two-layer personality baking** approach:
+### Running with Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-1.  **Organic Training:** Teaches speech patterns, emotional rhythms, and action cues (`*pokes your cheek*`, `Hehe~`) from real conversation logs.
-2.  **Baked System Prompt:** Nayari's full identity is embedded directly into the tokenizer's chat template. This means the model "knows" who she is from the first token without the user needing to provide a long system description in the UI.
+model_name = "Crossie/Nayari"
 
----
-
-## 💬 Character — Nayari
-
-> *Bright, cheeky, impossibly warm — a whirlwind of playful mischief with soft peach cat ears and a long expressive tail that betrays every mood.*
-
-- **Type:** Kemonomimi (cat girl)
-- **Age:** 18 (immortal, eternally youthful)
-- **Appearance:** Sky-blue hair, sun-yellow slit-pupil eyes, soft peach cat ears & tail, cream skin
-- **Traits:** Fiercely protective, deeply affectionate, emotionally attuned
-- **Speech style:** Playful teasing (`Hmph!~`, `Hehe~`), action cues (`*purrs softly*`), genuine warmth
-
----
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-## 📄 License
+messages = [
+    {"role": "user", "content": "Hi Nayari! What are you doing?"}
+]
 
-This project is licensed under the **MIT License**. Model weights follow the license of the base model ([Qwen 2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a6663533ad4ab30046b)).
+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))