Instructions to use ramyun/adelie-qwen-roleplay-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 ramyun/adelie-qwen-roleplay-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 ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

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 ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

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 ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

Use Docker

docker model run hf.co/ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use ramyun/adelie-qwen-roleplay-v2-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ramyun/adelie-qwen-roleplay-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": "ramyun/adelie-qwen-roleplay-v2-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

Ollama
How to use ramyun/adelie-qwen-roleplay-v2-gguf with Ollama:
```
ollama run hf.co/ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M
```
Unsloth Desktop

How to use ramyun/adelie-qwen-roleplay-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 ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

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": "ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ramyun/adelie-qwen-roleplay-v2-gguf with Docker Model Runner:
```
docker model run hf.co/ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M
```

Lemonade

How to use ramyun/adelie-qwen-roleplay-v2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

Run and chat with the model

lemonade run user.adelie-qwen-roleplay-v2-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ramyun/adelie-qwen-roleplay-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 ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

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 ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ramyun/adelie-qwen-roleplay-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 ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M

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 "ramyun/adelie-qwen-roleplay-v2-gguf:Q4_K_M" \
  --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"

AdelieAI · Qwen-Roleplay v2 (GGUF q4_k_m)

CPU-friendly q4_k_m quantization of the merged Qwen2.5-7B-Instruct + adelie-qwen-roleplay-v2-lora checkpoint. Ships in a single 4.4 GB file — runs on a laptop without a GPU.

Companion to the LoRA adapter at ramyun/adelie-qwen-roleplay-v2-lora. The LoRA repo is for FP16 + GPU mounting; this GGUF is for laptops / end-users without CUDA.

How to use

llama.cpp / llama-cpp-python

pip install llama-cpp-python --only-binary=:all: \
    --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu

huggingface-cli download ramyun/adelie-qwen-roleplay-v2-gguf \
    qwen-roleplay-v2.q4_k_m.gguf --local-dir ./models

from llama_cpp import Llama

llm = Llama(
    model_path="./models/qwen-roleplay-v2.q4_k_m.gguf",
    n_ctx=4096,
    n_threads=8,
)
out = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "당신은 판타지 세계의 냉소적인 잡화상 주인입니다."},
        {"role": "user", "content": "할인 좀 안 돼요?"},
    ],
    temperature=0.7,
)
print(out["choices"][0]["message"]["content"])

Or mount via the AdelieAI engine

huggingface-cli download ramyun/adelie-qwen-roleplay-v2-gguf \
    qwen-roleplay-v2.q4_k_m.gguf \
    --local-dir models/ours/qwen-roleplay-v2-gguf

MODEL_PATH=models/ours/qwen-roleplay-v2-gguf/qwen-roleplay-v2.q4_k_m.gguf \
PYTHONUTF8=1 .venv/Scripts/uvicorn core.api.app:app --port 8770

Quantization details


Source	merged FP16 (`Qwen2.5-7B-Instruct + adelie-qwen-roleplay-v2-lora`)
Method	llama.cpp `convert_hf_to_gguf.py` → `quantize` to `q4_k_m`
Original size	14.5 GB (FP16)
Quantized size	4.4 GB (3.25× compression)
Runtime VRAM	0 (pure CPU)
Runtime RAM	~5 GB

Style preservation verified: 4/5 persona prompts + 5/5 general prompts pass under greedy decoding (see recipe.md).

Recommended sampling

temperature=0.7 and top_p=0.9. Greedy decoding (temperature=0) sometimes diverges on q4_k_m — known quantization artifact, mitigated by sampling.

License

Inherits the Tongyi Qianwen License v1 from the base model (Qwen/Qwen2.5-7B-Instruct).

Citation

@software{adelieai_2026,
  title  = {AdelieAI: a persona engine for small-deployment LLMs},
  author = {ramyun},
  year   = {2026},
  url    = {https://github.com/southglory/AdelieAI}
}

Downloads last month: 112

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for ramyun/adelie-qwen-roleplay-v2-gguf

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(436)

this model