Instructions to use levanell/tsundere-qwen-v4-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use levanell/tsundere-qwen-v4-1.5B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf levanell/tsundere-qwen-v4-1.5B
# Run inference directly in the terminal:
llama cli -hf levanell/tsundere-qwen-v4-1.5B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf levanell/tsundere-qwen-v4-1.5B
# Run inference directly in the terminal:
llama cli -hf levanell/tsundere-qwen-v4-1.5B

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf levanell/tsundere-qwen-v4-1.5B
# Run inference directly in the terminal:
./build/bin/llama cli -hf levanell/tsundere-qwen-v4-1.5B

Use Docker

docker model run hf.co/levanell/tsundere-qwen-v4-1.5B

Llama new
LM Studio
Jan
Ollama
How to use levanell/tsundere-qwen-v4-1.5B with Ollama:
```
ollama run hf.co/levanell/tsundere-qwen-v4-1.5B
```
Unsloth Desktop

How to use levanell/tsundere-qwen-v4-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf levanell/tsundere-qwen-v4-1.5B

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "levanell/tsundere-qwen-v4-1.5B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use levanell/tsundere-qwen-v4-1.5B with Docker Model Runner:
```
docker model run hf.co/levanell/tsundere-qwen-v4-1.5B
```

Lemonade

How to use levanell/tsundere-qwen-v4-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull levanell/tsundere-qwen-v4-1.5B

Run and chat with the model

lemonade run user.tsundere-qwen-v4-1.5B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use levanell/tsundere-qwen-v4-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf levanell/tsundere-qwen-v4-1.5B

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default levanell/tsundere-qwen-v4-1.5B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use levanell/tsundere-qwen-v4-1.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf levanell/tsundere-qwen-v4-1.5B

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "levanell/tsundere-qwen-v4-1.5B" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

tsundere-qwen-v4

A Qwen2.5-1.5B-Instruct model finetuned to respond in a tsundere personality — reluctant, a little prickly, but genuinely caring underneath. Handles both emotional support conversations and technical questions (MongoDB, REST APIs, general coding).

Finetuned with Unsloth using LoRA on a mixed dataset of ~1,570 tsundere-style Q&A pairs. Exported as q8_0 GGUF for local inference.

What it can do

Emotional support and mental health conversations in tsundere style
Technical Q&A (databases, APIs, coding) in tsundere style
Reasoning responses with <think> tags on some questions

What it cannot do

Long conversations — 512 token context window. It will degrade or cut off on long inputs.
It is a 1.5B model. Don't expect deep reasoning or complex multi-step problems.
Not suitable for factual knowledge tasks — this is a personality/style finetune, not a knowledge finetune.

Training details

Field	Value
Base model	Qwen2.5-1.5B-Instruct
Finetuning method	LoRA (r=8, alpha=16)
Dataset size	1,572 rows
Epochs	3
Context length	512 tokens
Quantization	q8_0 GGUF
Framework	Unsloth

Example output

User: >>> I'm having trouble with my self-esteem.

Model: /think
Ugh, why did they have to say that now? I mean, it’s not like I wanted to hear about their feelings or anything... But still, it hurts me to think they’re feeling down… D-Don’t get the wrong idea! It's just… you shouldn't be so hard on yourself, okay?
/think
H-Hey, don’t go thinking too much about that stuff, alright?! I’m not saying I care or anything... b-but maybe try to remember you’re stronger than you think. J-Just… don’t let those doubts get the best of you, got it?

User: >>> How do I update a document in MongoDB?

Model: Use update_one()! You pass the query and your new data dictionary inside. If everything goes well, it updates right there without restarting the server or anything… stupid driver APIs!

Downloads last month: 53

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for levanell/tsundere-qwen-v4-1.5B

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

unsloth/Qwen2.5-1.5B-Instruct

Quantized

(14)

this model

levanell
/

tsundere-qwen-v4-1.5B

tsundere-qwen-v4

What it can do

What it cannot do

Training details

Example output

Model tree for levanell/tsundere-qwen-v4-1.5B

Dataset used to train levanell/tsundere-qwen-v4-1.5B