Instructions to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with Transformers:

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

pipe = pipeline("text-generation", model="CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0")
model = AutoModelForMultimodalLM.from_pretrained("CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 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 CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16
# Run inference directly in the terminal:
llama cli -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16
# Run inference directly in the terminal:
llama cli -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0: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 CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16
# Run inference directly in the terminal:
./llama-cli -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0: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 CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

Use Docker

docker model run hf.co/CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

LM Studio
Jan

vLLM

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

SGLang

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 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 "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with Ollama:
```
ollama run hf.co/CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16
```

Unsloth Studio

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 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 CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 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 CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 to start chatting

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

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": "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

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 "CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16" \
  --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"

Docker Model Runner
How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with Docker Model Runner:
```
docker model run hf.co/CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16
```

Lemonade

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-JP-Tuned-v2.0-BF16

List all available models

lemonade list

Hermes Agent

How to use CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

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 CloudGoat/Qwen3.5-0.8B-JP-Tuned-v2.0:BF16

Run Hermes

hermes

Atomic Chat

Qwen3.5-0.8B-JP-Tuned-v2.0

This model was created by merging two different Japanese-tuned Qwen3.5-0.8B models and the base model using DARE-TIES. The merge was performed in a way that avoids attention conflicts between the models. I used mergekit.

Benchmark

JCommonsenseQA: 67.11%
JNLI: 40.30%
JSQuAD: 47.68%
MARC-ja: 90.57%

Overall, performance improved by 6.02% compared to the base model and by 2.49% compared to v1.0, with notable gains observed in MARC-ja. The initial objective of specializing in "everyday conversation" has been achieved. However, the decline in scores for JNLI and JSQuAD indicates a reduction in logical reasoning capabilities and accuracy when handling long texts.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using Qwen/Qwen3.5-0.8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Holy-fox/Qwen3.5-0.8B-JP                  
      density: 0.80                                 
      weight: [0.5, 0.2, 0.4]                        
  - model: CloudGoat/Qwen3.5-0.8B-JP-Tuned-v1.1      
    parameters:
      density: 0.80
      weight: [0.5, 0.8, 0.6]                       
merge_method: dare_ties
base_model: Qwen/Qwen3.5-0.8B
parameters:
  int8_mask: true
  normalize: true                                    
dtype: bfloat16