Instructions to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf 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 rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K
# Run inference directly in the terminal:
llama cli -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K
# Run inference directly in the terminal:
llama cli -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

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 rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

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 rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

Use Docker

docker model run hf.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

LM Studio
Jan

vLLM

How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

Ollama
How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with Ollama:
```
ollama run hf.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K
```

Unsloth Studio

How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf 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 rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf 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 rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf to start chatting

How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with Docker Model Runner:
```
docker model run hf.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K
```

Lemonade

How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

Run and chat with the model

lemonade run user.qwen2.5-bakeneko-32b-instruct-v2-gguf-Q2_K

List all available models

lemonade list

Hermes Agent

How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

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 rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K

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 "rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf:Q2_K" \
  --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"

keisawada commited on Mar 23, 2025

Commit

263252c

verified ·

1 Parent(s): 5949469

Update README.md

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README.md +13 -1

README.md CHANGED Viewed

@@ -20,6 +20,14 @@ pipeline_tag: text-generation
 This model is a quantized model for [rinna/qwen2.5-bakeneko-32b-instruct-v2](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2) using [llama.cpp](https://github.com/ggerganov/llama.cpp). It is compatible with many llama.cpp based apps.
 See [rinna/qwen2.5-bakeneko-32b-instruct-v2](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2) for details about model architecture and data.
 * **Contributors**
@@ -27,6 +35,10 @@ See [rinna/qwen2.5-bakeneko-32b-instruct-v2](https://huggingface.co/rinna/qwen2.
     - [Xinqi Chen](https://huggingface.co/Keely0419)
     - [Kei Sawada](https://huggingface.co/keisawada)
 ---
 # Benchmarking
@@ -43,7 +55,7 @@ See [rinna/qwen2.5-bakeneko-32b-instruct-v2](https://huggingface.co/rinna/qwen2.
 | [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | 76.12 | 8.58 | 8.25
 | [rinna/qwq-bakeneko-32b](https://huggingface.co/rinna/qwq-bakeneko-32b) | 78.31 | 8.81 | 8.52
-For detailed benchmarking results, please refer to [rinna's LM benchmark page](https://rinnakk.github.io/research/benchmarks/lm/index.html).
 ---

 This model is a quantized model for [rinna/qwen2.5-bakeneko-32b-instruct-v2](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2) using [llama.cpp](https://github.com/ggerganov/llama.cpp). It is compatible with many llama.cpp based apps.
+| Model Type | Model Name
+| :-   | :-
+| Japanese Continual Pre-Training Model | Qwen2.5 Bakeneko 32B [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b)
+| Instruction-Tuning Model | Qwen2.5 Bakeneko 32B Instruct [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct)[[AWQ]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-awq)[[GGUF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-gptq-int4)
+| DeepSeek R1 Distill Qwen2.5 Merged Reasoning Model | DeepSeek R1 Distill Qwen2.5 Bakeneko 32B [[HF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b)[[AWQ]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-awq)[[GGUF]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gguf)[[GPTQ int8]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/deepseek-r1-distill-qwen2.5-bakeneko-32b-gptq-int4)
+| QwQ Merged Reasoning Model | QwQ Bakeneko 32B [[HF]](https://huggingface.co/rinna/qwq-bakeneko-32b)[[AWQ]](https://huggingface.co/rinna/qwq-bakeneko-32b-awq)[[GGUF]](https://huggingface.co/rinna/qwq-bakeneko-32b-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwq-bakeneko-32b-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwq-bakeneko-32b-gptq-int4)
+| QwQ Bakeneko Merged Instruction-Tuning Model | Qwen2.5 Bakeneko 32B Instruct V2 [[HF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2)[[AWQ]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-awq)[[GGUF]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gguf)[[GPTQ int8]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gptq-int8)[[GPTQ int4]](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2-gptq-int4)
 See [rinna/qwen2.5-bakeneko-32b-instruct-v2](https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2) for details about model architecture and data.
 * **Contributors**
     - [Xinqi Chen](https://huggingface.co/Keely0419)
     - [Kei Sawada](https://huggingface.co/keisawada)
+* **Release date**
+    February 19, 2025
 ---
 # Benchmarking
 | [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) | 76.12 | 8.58 | 8.25
 | [rinna/qwq-bakeneko-32b](https://huggingface.co/rinna/qwq-bakeneko-32b) | 78.31 | 8.81 | 8.52
+For detailed benchmarking results, please refer to [rinna's LM benchmark page (Sheet 20250319)](https://rinnakk.github.io/research/benchmarks/lm/index.html).
 ---