Instructions to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha 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 TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha # Run inference directly in the terminal: llama cli -hf TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha # Run inference directly in the terminal: llama cli -hf TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
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 TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha # Run inference directly in the terminal: ./build/bin/llama cli -hf TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
Use Docker
docker model run hf.co/TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
- Llama new
- LM Studio
- Jan
- vLLM
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
- Ollama
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with Ollama:
ollama run hf.co/TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
- Unsloth Desktop
- Pi
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
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": "TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with Docker Model Runner:
docker model run hf.co/TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
- Lemonade
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
Run and chat with the model
lemonade run user.Qwen3-4B-Instruct-2507-mahjong-alpha-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
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 TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
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 "TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha" \ --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"
Qwen3-4B-Instruct-2507-mahjong-alpha
Qwen3-4B-Instruct-2507-mahjong-alpha ๆฏไธไธชๅบไบ unsloth/Qwen3-4B-Instruct-2507 ่ฟ่ก QLoRA ๅพฎ่ฐ็็ซ็ด้บปๅฐๅ็ดๆจกๅ๏ผ้ขๅๅ้บปๅผ็ๅปบ่ฎฎไปปๅกใ
ๆจกๅๅฏๆ นๆฎ่พๅ ฅ็ๅบๆฌกไฟกๆฏใๆ็ใๅฏ้ฒใ็ๆฒณใ็ๆไธ้ฒๅฎไฟกๆฏ๏ผ่พๅบๅฝๅๆๅบๆๅบ็ไธๅผ ็ใ
ๅฝๅ็ๆฌไธป่ฆ้ขๅๅทฅๅ ท้ๆๅบๆฏ๏ผๆจ็่พๅบไธบๅๅผ ็ๆๆฌ๏ผไธๅ ๅซ่งฃ้ไฟกๆฏใ
ๆจกๅ็น็น
- ไปปๅก๏ผๅ้บป็ซ็ด้บปๅฐๅผ็ๅปบ่ฎฎ
- ๅบๅบงๆจกๅ๏ผ
unsloth/Qwen3-4B-Instruct-2507 - ๅพฎ่ฐๆนๅผ๏ผ
QLoRA - ่ฎญ็ปๆกๆถ๏ผ
Unsloth - ๅๅธๆ ผๅผ๏ผ
GGUF (F16) - ๆจ็ๆนๅผ๏ผ
llama.cpp - ็ปดๆค่
๏ผ
TTDXQ
้็จ่ๅด
ๆฌๆจกๅ้ขๅๅ้บปๅบๆฏ๏ผไธๅซ่ตคๅฎ็ใๅฝๅ็ๆฌไธๆณจไบ"ๅผ็ๅปบ่ฎฎ"่ฟไธๅไธไปปๅก๏ผไธๆไพๅฎๆดๅฏนๅฑ่งๅ๏ผไนไธๆไพๅฝน็งใๆ็นๆ่ฏฆ็ปๆป้ฒ่งฃ้ใ
ไฝฟ็จ้ๅถ
- ไป ๆฏๆๅผ็ๅปบ่ฎฎ
- ไธๆฏๆๅฎๆดๅฏนๅฑ่งๅ
- ไธๆฏๆๅฝน็งใๆ็นใ่ฟๆป้ฒๅฎ่งฃ้
- ไธไฟ่ฏ็ซ่ตๆๅฎๆๆๆ
- ไป ไพ็ ็ฉถไธๅญฆไน ไฝฟ็จ
็ฆๆญข็จ้
็ฆๆญขๅฐๆฌๆจกๅ็จไบ๏ผ
- ไฝๅผ
- ๅคๆ
- ไปฃๆ
- ็้ฑ่ตๅ่พ ๅฉ
ๆจกๅ่พๅ ฅ่พๅบ
่พๅ ฅๆ ผๅผ
ๆจกๅ่พๅ ฅไธบ็ปๆๅ่ช็ถ่ฏญ่จๅฑ้ขๆ่ฟฐใ็คบไพ๏ผ
[ๆ
ๆฏๅๆ]
- ็ๅฑ: ไธไธๅฑ๏ผไฝ ๆฏๅบๅฎถ (็ฌฌ1ๅทก๏ผ็ๅขไฝ69ๅผ )ใ
- ็ถๆ: ๅฝๅๆๅ 1/4 (ไธไธไฝๅทฎ 0)ใ
- ๅฎ็: 5ไธ
- ๅ็ฉๅฎถๅๆฐ: ไฝ ๆ 25ๅ, ไธๅฎถ: 25ๅ, ๅฏนๅฎถ: 25ๅ, ไธๅฎถ: 25ๅใ
- ไฝ ็ๆ็: 1ไธ 5ไธ 7ไธ 3็ญ 5็ญ 6็ญ 8็ญ 8็ญ 3็ดข 5็ดข 8็ดข ๅ ็ฝ ๅ
- ็ๆ: 5 ๅๅฌ๏ผ่ฟๅผ 82 ๅผ ใ
- ้ฒๅพก๏ผ
ๆๅฎๅ
จ็ๆพ้ณ็๏ผ11.3%
ๅนณๅๆพ้ณ็๏ผ18.5%
ๆๅฑ้ฉ็ๆพ้ณ็๏ผ25.9%
ๅบไธๅทฒ่ง็ไฟกๆฏ
ๅ็ฉๅฎถๅฏ้ฒไฟกๆฏ:ๆฌๅฎถๅฏ้ฒ๏ผๆ , ไธๅฎถๅฏ้ฒ๏ผๆ , ๅฏนๅฎถๅฏ้ฒ๏ผๆ , ไธๅฎถๅฏ้ฒ๏ผๆ
ๅ็ฉๅฎถ็ๆฒณไฟกๆฏ:ๆฌๅฎถ๏ผๆ , ไธๅฎถ๏ผๆ , ๅฏนๅฎถ๏ผๆ , ไธๅฎถ๏ผๆ
[ไปปๅก]
ๆ นๆฎๅฝๅๆ
ๆฏ๏ผ้ๆฉไธๅผ ๆๅบ่ฏฅๆๅบ็ๆ็ใ
่พๅบๆ ผๅผ
ๆจกๅ่พๅบไธฅๆ ผไธบ"ๅๅผ ็ๆๆฌ"๏ผไธๅธฆ"ๆ"ๅญ๏ผไธๅธฆ่งฃ้ใไพๅฆ๏ผ
็ฝ
ไฝฟ็จๆนๆณ
llama.cpp ๆจ็
llama-server -m Qwen3-4B-Instruct-2507-mahjong-alpha.gguf -c 2048
Python ๆจ็็คบไพ
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha"
)
tokenizer = AutoTokenizer.from_pretrained(
"TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha"
)
# ๅๅค่พๅ
ฅ
input_text = "[ๆ
ๆฏๅๆ]\n- ็ๅฑ: ไธไธๅฑ๏ผไฝ ๆฏๅบๅฎถ (็ฌฌ1ๅทก๏ผ็ๅขไฝ69ๅผ )ใ\n..."
# ๆจ็
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=10)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result) # ่พๅบ: ็ฝ
ๆฐๆฎ้
่ฎญ็ปๆฐๆฎไฝฟ็จ pjura/mahjong_board_states ็ 2018 ๅนด้จๅๆฐๆฎใ่ฏฅๆฐๆฎ้ๆฅๆบไบๅคฉ้ฃ้บปๅฐ็ๆธธ็ฉ่ฎฐๅฝ๏ผๆฏๆกๆฐๆฎๅ
ๅซ 511 ไธชๆฐๆฎ็น๏ผๆถต็ๆธธๆๅบ็กไฟกๆฏใๅฎ็ๆ็คบ็ใ่ง่ง็ฉๅฎถๆ็ใ็ฉๅฎถๅฏ้ฒใ็ๆฒณไฟกๆฏใ็ฉๅฎถ่็ใๅผ็ๅณ็ญ็ญใ
ๆฐๆฎๅค็
ๅฐๅๅงๆฐๆฎ่ฝฌๆขไธบไพฟไบ้ ่ฏป็่ช็ถ่ฏญ่จๆ่ฟฐๅฝขๅผ๏ผๅนถๆ นๆฎๆฐๆฎ่ฎก็ฎๅบๅทก็ฎๆฐใๅฎ้ ๅฎ็ใ็ฎๆๆพ้ณๅ่็ญไฟกๆฏใๆ นๆฎๅทก็ฎ่ฐๆดๆ ทๆฌๆฏไพ๏ผ
- 1~3 ๅทก๏ผ15%
- 4~6 ๅทก๏ผ20%
- 7~12 ๅทก๏ผ35%
ๆ็ปไฝฟ็จ 192000 ๆกๆ ทๆฌ๏ผๆชๆททๅ
ฅ้็จๆไปคๆฐๆฎๆ่ชๅปบๆฐๆฎใ
- ่ฎญ็ป้๏ผ
192000 - ้ช่ฏ้๏ผ
2000 - ๆต่ฏ้๏ผ
2019 ๅนดๆฐๆฎๆ้ๆฝๅ - ่ฎญ็ป / ้ช่ฏ / ๆต่ฏ๏ผๅฎๅ จไบไธ้ๅ
ๆฐๆฎ้ๅผ็จ
@dataset{mahjong_board_states,
title = {MahJong Board States Dataset},
author = {Patrick Jura},
year = {2024},
url = {https://huggingface.co/datasets/pjura/mahjong_board_states}
}
่ฎญ็ปไฟกๆฏ
ๆจกๅ้ ็ฝฎ
- ๅบ็กๆจกๅ๏ผ
unsloth/Qwen3-4B-Instruct-2507 - ่ฎญ็ปๅ ่ฝฝ็ฒพๅบฆ๏ผ
4bit - ๅพฎ่ฐๆนๅผ๏ผ
QLoRA - ่ฎญ็ปๆกๆถ๏ผ
Unsloth - Max sequence length๏ผ
2048
LoRA ๅๆฐ
- Rank๏ผ
128 - Alpha๏ผ
256 - ็ฎๆ ๆจกๅ๏ผๅ จ้จ
่ฎญ็ป่ถ ๅๆฐ
- Learning rate๏ผ
1e-4 - LR scheduler๏ผ
cosine - Batch size๏ผ
64 - ๅๅกๆนๆฌก๏ผ
2 - ๆขฏๅบฆ็ดฏ็งฏๆญฅๆฐ๏ผ
32 - Training steps๏ผ
3000 - Warmup steps๏ผ
300 - Random seed๏ผ
3407 - ๅ ่ฝฝๆไผๆฃๆฅ็น๏ผๆฏ
่ฎญ็ปๆถ้ด
- ๆปๆถ้ฟ๏ผ็บฆ 16.44 ๅฐๆถ
่ฏๆต็ปๆ
ไธๆฐๆฎๅบๅผ็ๅจไฝๅฏนๆฏ
ๆจ็ๅๆฐ๏ผTemperature=0.1, Top_P=0.1
่ฏๆตๆๆ ่ฏดๆ๏ผ
- ๅพๅ๏ผๆปกๅ 500 ๅ๏ผๆฏไธชๆ ทๆฌๆญฃ็กฎๅพ 1 ๅ๏ผ้่ฏฏๅพ 0 ๅ๏ผ
- ๆ ทๆฌๅ จๅฏน็๏ผ3 ๆฌกๆต่ฏๅไธๆต่ฏ้็ปๆไธ่ด็ๆ ทๆฌๅ ๅ จ้จๆ ทๆฌ็ๆฏไพ
- ๆ ทๆฌ้ถๅ็๏ผ3 ๆฌกๆต่ฏๅไธๆต่ฏ้็ปๆไธ็ฌฆ็ๆ ทๆฌๅ ๅ จ้จๆ ทๆฌ็ๆฏไพ
็ๆๆต่ฏ
| ๆจกๅ | ๆนๆณ | ๅพๅ | ๆ ทๆฌๅ จๅฏน็ | ๆ ทๆฌ้ถๅ็ |
|---|---|---|---|---|
| Qwen3-4B | ๆ็คบ่ฏๅทฅ็จ | 50.21 | 6.60% | 86.13% |
| Qwen3-4B | ๅพฎ่ฐ | 229.66 | 45.87% | 53.93% |
| DeepSeek-V3.2 | ๆ็คบ่ฏๅทฅ็จ | 181.66 | 21.40% | 46.33% |
้ฒๅฎๆต่ฏ
| ๆจกๅ | ๆนๆณ | ๅพๅ | ๆ ทๆฌๅ จๅฏน็ | ๆ ทๆฌ้ถๅ็ |
|---|---|---|---|---|
| Qwen3-4B | ๆ็คบ่ฏๅทฅ็จ | 53.55 | 6.17% | 84.43% |
| Qwen3-4B | ๅพฎ่ฐ | 239.89 | 47.93% | 52.00% |
| DeepSeek-V3.2 | ๆ็คบ่ฏๅทฅ็จ | 172.00 | 16.00% | 46.80% |
็ปผๅๆต่ฏ
| ๆจกๅ | ๆนๆณ | ๅพๅ | ๆ ทๆฌๅ จๅฏน็ | ๆ ทๆฌ้ถๅ็ |
|---|---|---|---|---|
| Qwen3-4B | ๆ็คบ่ฏๅทฅ็จ | 53.44 | 0.60% | 84.40% |
| Qwen3-4B | ๅพฎ่ฐ | 233.33 | 46.53% | 53.20% |
| DeepSeek-V3.2 | ๆ็คบ่ฏๅทฅ็จ | 179.44 | 18.07% | 44.93% |
ไธ Mortal ๅฏนๆฏ
ๆจ็ๅๆฐ๏ผTemperature=0.6, Top_P=0.95
ๆต่ฏ1๏ผๅ จ้จๅทก็ฎๆฐๆฎ
- ๆ ทๆฌๆฐ๏ผ3000
- Top-1 ๅ็กฎ็๏ผ50.73%
- Top-3 ๅ็กฎ็๏ผ83.37%
ๆต่ฏ2๏ผๅป้คๆฉๅทกๆฐๆฎ
- ๆๆๆ ทๆฌๆฐ๏ผ3000
- Top-1 ๅ็กฎ็๏ผ48.70%
- Top-3 ๅ็กฎ็๏ผ79.20%
ๆณจ๏ผMortal ๆฏๅฝๅๅผๆบๆๅผบ็็ซ็ด้บปๅฐ AI ไนไธ
ไปๅบ้พๆฅ
- GitHub๏ผhttps://github.com/ttdxq/Qwen3-4B-Instruct-2507-mahjong-alpha
- Hugging Face๏ผhttps://huggingface.co/TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
License
ๆฌๆจกๅ้ตๅพช Apache License 2.0 ่ฎธๅฏ่ฏใ
่ฎญ็ปๆฐๆฎๆฅ่ช pjura/mahjong_board_states๏ผๅ
ถ่ฎธๅฏ่ฏไธบ CC BY 4.0๏ผไฝฟ็จๆถ่ฏทไฟ็็ธๅบ็ฝฒๅไธๅผ็จใ
Acknowledgements
ๆ่ฐขไปฅไธๅผๆบ่ตๆบ๏ผ
unsloth/Qwen3-4B-Instruct-2507pjura/mahjong_board_statesMortal
- Downloads last month
- 30
We're not able to determine the quantization variants.
Model tree for TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
Base model
Qwen/Qwen3-4B-Instruct-2507