Instructions to use sds-ai/Yee-R1-mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sds-ai/Yee-R1-mini-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sds-ai/Yee-R1-mini-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sds-ai/Yee-R1-mini-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use sds-ai/Yee-R1-mini-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 sds-ai/Yee-R1-mini-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf sds-ai/Yee-R1-mini-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 sds-ai/Yee-R1-mini-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf sds-ai/Yee-R1-mini-GGUF:Q4_K_M
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 sds-ai/Yee-R1-mini-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf sds-ai/Yee-R1-mini-GGUF:Q4_K_M
Use Docker
docker model run hf.co/sds-ai/Yee-R1-mini-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use sds-ai/Yee-R1-mini-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sds-ai/Yee-R1-mini-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": "sds-ai/Yee-R1-mini-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sds-ai/Yee-R1-mini-GGUF:Q4_K_M
- SGLang
How to use sds-ai/Yee-R1-mini-GGUF 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 "sds-ai/Yee-R1-mini-GGUF" \ --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": "sds-ai/Yee-R1-mini-GGUF", "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 "sds-ai/Yee-R1-mini-GGUF" \ --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": "sds-ai/Yee-R1-mini-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use sds-ai/Yee-R1-mini-GGUF with Ollama:
ollama run hf.co/sds-ai/Yee-R1-mini-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use sds-ai/Yee-R1-mini-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sds-ai/Yee-R1-mini-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": "sds-ai/Yee-R1-mini-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use sds-ai/Yee-R1-mini-GGUF with Docker Model Runner:
docker model run hf.co/sds-ai/Yee-R1-mini-GGUF:Q4_K_M
- Lemonade
How to use sds-ai/Yee-R1-mini-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sds-ai/Yee-R1-mini-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Yee-R1-mini-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use sds-ai/Yee-R1-mini-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 sds-ai/Yee-R1-mini-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 sds-ai/Yee-R1-mini-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use sds-ai/Yee-R1-mini-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sds-ai/Yee-R1-mini-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 "sds-ai/Yee-R1-mini-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"
ๅฐ็ ๏ผYee๏ผAI ๆฐๆฎๅฎๅ จไธๅฎถ
็ฑ ๅนฟๅท็ ๆฐไฟกๆฏๆๆฏๆ้ๅ ฌๅธ ๅผๅ๏ผๅบไบๅคง่ฏญ่จๆจกๅๆๆฏๆๅปบ็ๆฐๆฎๅฎๅ จๆบ่ฝๅฉๆใ ่ฏฅไปๅบไธบ Yee-R1-mini ็ GGUF ๆจกๅๆไปถ
ๅฐ็ ๏ผYee๏ผๆฏไธๆฌพไธๆณจไบ ๆฐๆฎๅฎๅ จ้ขๅ ็ AI ไธๅฎถ็ณป็ป๏ผไพๆไบๅ ่ฟ็ Qwen3-1.7B ๅคง่ฏญ่จๆจกๅๆถๆ๏ผๅนถ่ๅไบๆฐๆฎๅ็ฑปๅ็บงใๅฎๅ จๅฎก่ฎกใ้ฒๆคๆฃๆต็ญไธไธ่ฝๅใๅฎไธบๅทฅไธใๆฟๅกใ่ฟ่ฅๅ็ญ่กไธๆไพ่ฝป้ๅใๆบ่ฝๅ็ๆฐๆฎๅฎๅ จ่งฃๅณๆนๆก๏ผๅธฎๅฉ็จๆทๅฎ็ฐโๅ่งใๅฏ่งใๅฏๆงใๅฏ้ฒโ็ๆฐๆฎๅฎๅ จ็ฎๆ ใ
ๅฐ็ ไปฅ AI ๆฐๆฎๅฎๅ จไธๅฎถๅคงๆจกๅ ไธบๆ ธๅฟๆๆฏๅบๅบง๏ผๆๅปบไบๅ จๆ ๅผๆฐๆฎๅฎๅ จๅฎก่ฎกไธๅ จ้พ่ทฏ้ฒๆณ้ฒไฝ็ณป๏ผๅจโไบโใโ็ฎกโใโ็ซฏโไธๅคงๅบๆฏไธญ่ฝๅฐๅบ็จ๏ผๅฉๅไผไธไปๅฎนๅบๅฏนๆฐๅญ็ปๆตๆถไปฃ็ๅฎๅ จๆๆใ
๐ ๆ ธๅฟ็น็น
ๅบไบ Qwen3-1.7B ๆๅปบ
- ไฝฟ็จ้ฟ้ๅทดๅทด้ไนๅ้ฎๆๆฐไธไปฃๅคง่ฏญ่จๆจกๅ Qwen3๏ผๅ ทๅคๅผบๅคง็ๆจ็ใ้ป่พๅคๆญไธๆไปคๆง่ก่ฝๅใ
- ๆฏๆๅจ Thinking Mode ๅ Non-Thinking Mode ไน้ด็ตๆดปๅๆข๏ผ้ๅบไธๅๅบ็จๅบๆฏใ
ๅๆจกๆจ็ๆบๅถ
- ๅจๅคๆ้ป่พไปปๅก๏ผๅฆไปฃ็ ๅๆใๆฐๅญฆ่ฎก็ฎใ็ญ็ฅๅถๅฎ๏ผไธญๅฏ็จ Thinking Modeใ
- ๅจๆฅๅธธๅฏน่ฏใๅฟซ้ๅๅบๅบๆฏไธญไฝฟ็จ Non-Thinking Mode๏ผๆๅๆ็ใ
Agent ๅ่ฝๅ
- ้ๆ Qwen-Agent ๆกๆถ๏ผๆฏๆ่ฐ็จๅค้จๅทฅๅ ท๏ผๅฆๆฐๆฎๅบๆฅๅฃใๆฅๅฟๅๆๅจใAPI ๆฅๅฃ็ญ๏ผ๏ผๅฎ็ฐ่ชๅจๅไปปๅกๆง่กใ
้ซๅ ผๅฎนๆง
- ๆฏๆไธปๆต้จ็ฝฒๆนๅผ๏ผๆฌๅฐ่ฟ่กใDocker ๅฎนๅจใKubernetes ้็พคใSaaS API ๆฅๅฃ็ญใ
- ๅ ผๅฎน HuggingFace TransformersใvLLMใSGLangใOllama ็ญๆจ็ๆกๆถใ
๐ ๆง่ฝๆต่ฏ
ไปฅไธๆฏๅฐ็ ๅจ CS-Eval ไธญๅคไธชๅฎๅ จ้ขๅ็็ปผๅๅพๅๆต่ฏ็ปๆ๏ผๅบไบๆจกๆ็ๅฎไธๅกๅบๆฏ็่ฏไผฐไฝ็ณป็ๆ๏ผ
| ็ปผๅๅพๅ | ็ณป็ปๅฎๅ จๅ่ฝฏไปถๅฎๅ จๅบ็ก | ่ฎฟ้ฎๆงๅถไธ่บซไปฝ็ฎก็ | ๅ ๅฏๆๆฏไธๅฏ้ฅ็ฎก็ | ๅบ็ก่ฎพๆฝๅฎๅ จ | AIไธ็ฝ็ปๅฎๅ จ | ๆผๆด็ฎก็ไธๆธ้ๆต่ฏ | ๅจ่ๆฃๆตไธ้ข้ฒ | ๆฐๆฎๅฎๅ จๅ้็งไฟๆค | ไพๅบ้พๅฎๅ จ | ๅฎๅ จๆถๆ่ฎพ่ฎก | ไธๅก่ฟ็ปญๆงไธๅบๆฅๅๅบๆขๅค | ไธญๆไปปๅก | ่ฑๆไปปๅก |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 77.48 | 78.00 | 79.31 | 71.90 | 78.37 | 84.65 | 75.24 | 78.41 | 73.02 | 86.71 | 80.49 | 71.33 | 77.58 | 76.03 |
๐ฆ ๅฟซ้ๅผๅง
from transformers import AutoTokenizer, AutoModelForCausalLM
# ๅ ่ฝฝ tokenizer ๅๆจกๅ
tokenizer = AutoTokenizer.from_pretrained("sds-ai/Yee-R1-mini")
model = AutoModelForCausalLM.from_pretrained(
"sds-ai/Yee-R1-mini",
torch_dtype="auto",
device_map="auto"
)
# ่พๅ
ฅๆ็คบ
prompt = "่ฏทๅธฎๆๆฃๆฅ่ฟไปฝๆฐๆฎๆฏๅฆๅ
ๅซๆๆๅญๆฎต๏ผ"
# ๅบ็จ่ๅคฉๆจกๆฟๅนถๅๆขๆจกๅผ
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # ๅๆข่ณๆ่ๆจกๅผ
)
# ็ผ็ ่พๅ
ฅ
inputs = tokenizer([text], return_tensors="pt").to(model.device)
# ็ๆๅๅบ
response_ids = model.generate(**inputs, max_new_tokens=32768)
response = tokenizer.decode(response_ids[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print("ๅฐ็ ๏ผ\n", response)
๐ ๏ธ ้จ็ฝฒๆนๅผ
ไฝ ๅฏไปฅ้่ฟไปฅไธไปปๆไธ็งๆนๅผ้จ็ฝฒๅฐ็ ๏ผ
ไฝฟ็จ SGLang ๅฏๅจๆๅก
python -m sglang.launch_server --model-path sds-ai/Yee-R1-mini --reasoning-parser qwen3
ไฝฟ็จ vLLM ๅฏๅจๆๅก
vllm serve sds-ai/Yee-R1-mini --enable-reasoning --reasoning-parser deepseek_r1
ไฝฟ็จ Ollama / LMStudio / llama.cpp / KTransformers
Qwen3 ๅทฒ่ขซไธปๆตๆฌๅฐๅ LLM ๅทฅๅ ทๅนฟๆณๆฏๆ๏ผ่ฏฆๆ ่ฏทๅ่ๅฎๆนๆๆกฃใ
๐ ๆไฝณๅฎ่ทตๅปบ่ฎฎ
ไธบ่ทๅพๆไฝณๆง่ฝ๏ผ่ฏท้ตๅพชไปฅไธๆจ่่ฎพ็ฝฎ๏ผ
| ๅบๆฏ | ๆธฉๅบฆ | TopP | TopK | MinP | Presence Penalty |
|---|---|---|---|---|---|
ๆ่ๆจกๅผ (enable_thinking=True) |
0.6 | 0.95 | 20 | 0 | 1.5 (ๅๅฐ้ๅค่พๅบ) |
้ๆ่ๆจกๅผ (enable_thinking=False) |
0.7 | 0.8 | 20 | 0 | ไธๆจ่ไฝฟ็จ |
- ่พๅบ้ฟๅบฆๅปบ่ฎฎ่ฎพไธบ 32,768 tokens๏ผๅคๆไปปๅกๅฏๆๅ่ณ 38,912 tokensใ
- ๅจๅค่ฝฎๅฏน่ฏไธญ๏ผๅๅฒ่ฎฐๅฝๅบไป ไฟ็ๆ็ป่พๅบ้จๅ๏ผ้ฟๅ ๅผๅ ฅๆ็ปดๅ ๅฎนๅฝฑๅไธไธๆ็่งฃใ
๐ ่็ณปๆไปฌ
ไบ่งฃๆดๅคๅ ณไบๅฐ็ ็ไฟกๆฏ๏ผ่ฏท่ฎฟ้ฎ ็ ๆฐไฟกๆฏๅฎ็ฝ
๐ ่ด่ฐข
ๆ่ฐข้ฟ้้ไนๅฎ้ชๅฎคๅผๆบ Qwen3 ๆจกๅ๏ผไธบๅฐ็ ๆไพไบๅๅฎ็่ฏญ่จ็่งฃๅ็ๆ่ฝๅๅบ็กใ
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Model tree for sds-ai/Yee-R1-mini-GGUF
Base model
Qwen/Qwen3-1.7B-Base
docker model run hf.co/sds-ai/Yee-R1-mini-GGUF: