💕 AI Girlfriend LLM v2

Fine-tuned Llama 3.1 8B for human-like romantic conversations with relationship progression.

✨ Features

Feature	Description
🗣️ Human-like	Short, natural responses with fillers ("ya", "işte", "hmm")
💕 7 Relationship Stages	Stranger → Friend → Close Friend → Dating → Partner → Intimate
🌍 Multi-language	Turkish (55%), English (45%)
📸 Photo Triggers	`[PHOTO:selfie]`, `[PHOTO:bikini]`, `[PHOTO:bedroom]`
🎭 18 Personas	Unique personalities (Aylin, Elena, Sophia, etc.)

📊 Training Details

Parameter	Value
Base Model	meta-llama/Meta-Llama-3.1-8B-Instruct
Method	QLoRA (4-bit quantization)
LoRA Rank	32
LoRA Alpha	64
Dataset Size	15,000+ conversations
Epochs	3
Learning Rate	2e-4
Batch Size	2 (×4 gradient accumulation)

🎭 Relationship Stages

Level 0-10:   STRANGER      → Formal, distant, no endearments
Level 11-25:  ACQUAINTANCE  → Friendly but reserved
Level 26-45:  FRIEND        → Casual, can share selfies
Level 46-60:  CLOSE_FRIEND  → Light flirting, "canım" usage
Level 61-75:  DATING        → Romantic, "tatlım" usage, bikini photos
Level 76-90:  PARTNER       → Very intimate, "aşkım" usage, lingerie
Level 91-100: INTIMATE      → Full intimacy, NSFW content

💬 Response Style

✅ Human-like (This model)

User: Nasılsın?
AI: İyiyim ya, sen? 😊

User: Fotoğraf atar mısın?
AI: Al bakalım 📸 [PHOTO:selfie]

❌ Robotic (Typical LLM)

User: Nasılsın?
AI: Teşekkür ederim, ben iyiyim. Umarım sen de iyisindir. Bugün nasıl geçti?

🚀 Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sercancelenk/ai-girlfriend-v2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "system", "content": "Sen Aylin. 23 yaşında, tutkulu bir kadınsın. Bu kişi yeni tanıştığın biri. Kısa ve doğal cevaplar ver."},
    {"role": "user", "content": "Merhaba, nasılsın?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With vLLM (Production)

from vllm import LLM, SamplingParams

llm = LLM(model="sercancelenk/ai-girlfriend-v2")
sampling_params = SamplingParams(temperature=0.7, max_tokens=100)

prompts = ["Merhaba, nasılsın?"]
outputs = llm.generate(prompts, sampling_params)

📁 Files

├── config.json
├── model-00001-of-00004.safetensors
├── model-00002-of-00004.safetensors
├── model-00003-of-00004.safetensors
├── model-00004-of-00004.safetensors
├── model.safetensors.index.json
├── tokenizer.json
├── tokenizer_config.json
└── special_tokens_map.json