Instructions to use subsectmusic/Riko2.5.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use subsectmusic/Riko2.5.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="subsectmusic/Riko2.5.1")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("subsectmusic/Riko2.5.1", dtype="auto")

llama-cpp-python

How to use subsectmusic/Riko2.5.1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="subsectmusic/Riko2.5.1",
	filename="unsloth.BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use subsectmusic/Riko2.5.1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf subsectmusic/Riko2.5.1:BF16
# Run inference directly in the terminal:
llama-cli -hf subsectmusic/Riko2.5.1:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf subsectmusic/Riko2.5.1:BF16
# Run inference directly in the terminal:
llama-cli -hf subsectmusic/Riko2.5.1:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf subsectmusic/Riko2.5.1:BF16
# Run inference directly in the terminal:
./llama-cli -hf subsectmusic/Riko2.5.1:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf subsectmusic/Riko2.5.1:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf subsectmusic/Riko2.5.1:BF16

Use Docker

docker model run hf.co/subsectmusic/Riko2.5.1:BF16

LM Studio
Jan

vLLM

How to use subsectmusic/Riko2.5.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "subsectmusic/Riko2.5.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "subsectmusic/Riko2.5.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/subsectmusic/Riko2.5.1:BF16

SGLang

How to use subsectmusic/Riko2.5.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "subsectmusic/Riko2.5.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "subsectmusic/Riko2.5.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "subsectmusic/Riko2.5.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "subsectmusic/Riko2.5.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use subsectmusic/Riko2.5.1 with Ollama:
```
ollama run hf.co/subsectmusic/Riko2.5.1:BF16
```

Unsloth Studio new

How to use subsectmusic/Riko2.5.1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for subsectmusic/Riko2.5.1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for subsectmusic/Riko2.5.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for subsectmusic/Riko2.5.1 to start chatting

Docker Model Runner
How to use subsectmusic/Riko2.5.1 with Docker Model Runner:
```
docker model run hf.co/subsectmusic/Riko2.5.1:BF16
```

Lemonade

How to use subsectmusic/Riko2.5.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull subsectmusic/Riko2.5.1:BF16

Run and chat with the model

lemonade run user.Riko2.5.1-BF16

List all available models

lemonade list

🎌 Riko 2.5 - Tsundere AI Assistant

A fine-tuned Qwen 2.5 7B model that roleplays as Riko, a tsundere character who acts tough and dismissive but is caring deep down.

🚀 Quick Start with Ollama

# Pull the model
ollama pull subsectmusic/Riko2.5.1

# Start chatting
ollama run subsectmusic/Riko2.5.1

# Example conversation
ollama run subsectmusic/Riko2.5.1 "Hi Riko, how are you today?"

📋 Model Details

Base Model: Qwen 2.5 7B Instruct
Fine-tuning: Supervised Fine-Tuning (SFT) with Unsloth
Format: GGUF Q4_K_M (4.68 GB) - Optimized for Ollama
Character: Tsundere personality (tough exterior, caring interior)

🎭 Character Personality

Riko exhibits classic tsundere behavior:

😤 Acts tough and dismissive initially
💢 Denies caring ("It's not like I care!")
😳 Gets flustered when her caring side shows
🙄 Uses "dummy," "idiot" endearingly
❤️ Gradually warms up in conversations

💬 Example Conversations

User: Do you like sunny days?
Riko: I guess sunny days are okay… but who needs that bright, annoying sunlight all the time anyway? It's just… too hot! But if you like them so much, I suppose that's fine… not that I'm jealous of the sun or anything!

User: Hey Riko, what are you up to today?
Riko: Oh, i just recently picked up a new hobby. Why do you ask? I wont let you on on it, if ist that what you want.

🛠️ Usage in Other Tools

LM Studio

Download unsloth.Q4_K_M.gguf (4.68 GB)

llama.cpp

./main -m unsloth.Q4_K_M.gguf -p "User: Hi Riko!\nRiko: " --temp 0.7

Text Generation WebUI

Load the unsloth.Q4_K_M.gguf file directly

⚡ Performance

Model Size: 4.68 GB (Q4_K_M quantized)
Memory Usage: ~6-8 GB RAM recommended
Speed: Fast inference on CPU/GPU
Quality: High quality responses with efficient compression

🔧 Technical Specs

Architecture: Qwen 2.5 Transformer
Context Length: 2048 tokens
Vocabulary: 152k tokens
Quantization: Q4_K_M (4-bit with higher quality)
Training Time: ~8 minutes on Colab T4

📁 Files Included

unsloth.Q4_K_M.gguf - Main quantized model (4.68 GB) ⭐ Recommended
unsloth.BF16.gguf - Full precision (15.2 GB)
Tokenizer files for compatibility
Config files for proper loading

⚠️ Usage Notes

Optimized for conversational, casual interactions
Best results with tsundere/anime-style roleplay
May not perform as well for technical tasks
Responds better to friendly, informal prompts

🎯 Recommended Settings

Ollama/LM Studio:

Temperature: 0.7-0.9
Top-p: 0.9
Max tokens: 150-300

For more creative responses:

Temperature: 0.8-1.0
Top-p: 0.95

📜 License

Apache 2.0 - Free to use, modify, and distribute!

🙏 Credits

Base Model: Qwen 2.5 by Alibaba
Fine-tuning: Unsloth framework
Training: Custom tsundere conversation dataset

🎌 Enjoy chatting with Riko! Remember, she's tough on the outside but sweet on the inside!

Downloads last month: 20

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit