---
base_model: Qwen/Qwen3-4B-Instruct
tags:
- text-generation-inference
- transformers
- qwen3
- gguf
- ollama
- tools
- function-calling
- character-roleplay
- tsundere
- conversational-ai
- fine-tuned
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
---

# 🦊 QwRiko3-4B-Instruct-2507 — Tsundere Kitsune AI (GGUF • Ollama • Tools)

<div align="center">
  <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
</div>

## 📋 Model Overview

**QwRiko3-4B-Instruct-2507** is a conversational AI model fine-tuned to embody **Riko**, a tsundere kitsune character. This release targets **GGUF** for **Ollama** first, with solid **tool calling** support when run via Ollama’s tools API. A PyTorch build (Transformers) is also supported.

- **Model ID (this repo):** `subsectmusic/qwriko3-4b-instruct-2507`
- **Primary format:** **GGUF** (Ollama-compatible)
- **Alt format:** PyTorch (Transformers)
- **Base Model:** `Qwen/Qwen3-4B-Instruct`
- **Parameters:** ~4B
- **License:** Apache-2.0 (repo)
- **Project:** Project Horizon LLM
- **Developer:** @subsectmusic
- **Training Framework:** Unsloth + TRL (SFT)

## 🎭 Character Profile: Riko

- **Tsundere cadence:** “It’s not like I like you or anything… b-baka!”  
- **Kitsune vibes:** fox-spirit mischief + sly wisdom  
- **Emotional core:** tough shell, soft center  
- **Style:** snappy, teasing, ultimately caring

---

## 🚀 Quick Start (Ollama • GGUF)

> These steps assume you have a local GGUF file named `qwriko3-4b-instruct-2507.Q4_K_M.gguf` in the working directory. If your filename differs, update the `FROM` path in the Modelfile accordingly.

1) **Create a Modelfile** (exact content below is also saved as `Modelfile` in this package):

```Dockerfile
# Modelfile
FROM ./qwriko3-4b-instruct-2507.Q4_K_M.gguf
PARAMETER num_ctx 8192
# (Optional) you can set temperature/top_p/etc. via `ollama run -p` or the API.
```

2) **Create the Ollama model**:

```bash
ollama create qwriko3-4b-instruct-2507 -f Modelfile
```

3) **Chat**:

```bash
ollama run qwriko3-4b-instruct-2507 "Riko, give me a playful hello."
```

### Tool Calling with Ollama (cURL)

```bash
curl http://localhost:11434/api/chat -d '{
  "model": "qwriko3-4b-instruct-2507",
  "messages": [
    { "role": "user", "content": "What is the weather today in Toronto?" }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_current_weather",
        "description": "Get the current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The location to get the weather for, e.g. Toronto"
            },
            "format": {
              "type": "string",
              "description": "Temperature units",
              "enum": ["celsius", "fahrenheit"]
            }
          },
          "required": ["location", "format"]
        }
      }
    }
  ]
}'
```

### Tool Calling with Ollama (Python)

A complete, ready-to-run example is saved as `tools_demo.py` in this package. It defines a couple of functions and lets the model call them. You can run it after installing the Python client:

```bash
pip install -U ollama
python tools_demo.py
```

---

## 🧪 Quick Start (Transformers • PyTorch)

```python
# Requirements:
#   pip install "transformers>=4.42.0" "torch>=2.1.0" accelerate
#   (CUDA recommended; CPU works but is slower.)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "subsectmusic/qwriko3-4b-instruct-2507"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are Riko, a tsundere kitsune AI. Be witty, teasing, but with hidden warmth."},
    {"role": "user", "content": "Hey Riko, how are you today?"}
]

if hasattr(tokenizer, "apply_chat_template"):
    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt"
    ).to(model.device)
else:
    prompt = (
        "System: You are Riko, a tsundere kitsune AI. Be witty, teasing, but with hidden warmth.\n"
        "User: Hey Riko, how are you today?\n"
        "Assistant:"
    )
    inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)

gen = model.generate(
    inputs,
    max_new_tokens=256,
    temperature=0.85,
    top_p=0.9,
    top_k=50,
    repetition_penalty=1.1,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
out = tokenizer.decode(gen[0][inputs.shape[1]:], skip_special_tokens=True)
print("\nRiko:", out.strip())
```

---

## 💡 Use Cases

- Character roleplay & entertainment  
- Creative writing in a tsundere voice  
- Personality-driven chatbots  
- Research on alternating-turn distillation & style transfer

## 🔬 Training Summary (SFT)

- **Format:** ShareGPT-style → Alpaca single-turn pairs  
- **Teachers:** Kimi K2 (odd) + Horizon Beta (even)  
- **Focus:** Tsundere kitsune persona, witty banter, emotional subtext  
- **Curation:** Manual filtering for tone & safety

Example SFT settings:

```yaml
Training Framework: Unsloth + TRL SFTTrainer
Base Model: Qwen/Qwen3-4B-Instruct
Batch Size: 2 per device
Gradient Accumulation: 4
Learning Rate: 2e-4
Optimizer: AdamW 8-bit
Weight Decay: 0.01
Scheduler: Linear
Max Steps: 100+
Warmup Steps: 5
Sequence Length: up to model context
Precision: fp16
```

## 📊 Specs

| Attribute        | Details                       |
|------------------|-------------------------------|
| Architecture     | Qwen3 Transformer             |
| Parameters       | ~4B                           |
| Base             | Qwen/Qwen3-4B-Instruct        |
| Context Length   | Base-dependent (Qwen3 config) |
| Formats          | **GGUF (Ollama)**; PyTorch    |
| Framework        | PyTorch + Transformers        |
| Optimization     | Unsloth-accelerated SFT       |
| Style            | Tsundere kitsune (Riko)       |

## 🎯 Recommended Inference Settings

```python
generation_config = {
    "max_new_tokens": 256,
    "temperature": 0.85,
    "top_p": 0.9,
    "top_k": 50,
    "repetition_penalty": 1.1,
    "do_sample": True,
    "pad_token_id": tokenizer.eos_token_id,
    "eos_token_id": tokenizer.eos_token_id
}
```

## ⚠️ Notes

- In-character style can color responses to factual queries  
- Compact 4B size benefits from clear prompts for complex tasks  
- Quantization can slightly affect nuance

## 🔒 Ethics

- Entertainment & creative use; not professional advice  
- Follow platform/community guidelines

## 📚 Citation

```bibtex
@model{qwriko3-4b-instruct-2507,
  title={QwRiko3-4B-Instruct-2507: Tsundere Kitsune AI},
  author={subsectmusic},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/subsectmusic/qwriko3-4b-instruct-2507}
}
```

## 🤝 Acknowledgments

- Kimi K2 & Horizon Beta (teachers)  
- Project Horizon LLM (methodology)  
- Unsloth, Qwen Team, Hugging Face / TRL  
- Ollama (GGUF runtime)

---

<div align="center">
  <b>Made with ❤️ using Unsloth</b><br>
  <i>Training AI personalities, one tsundere at a time!</i>
</div>