Instructions to use URajinda/ShweYon_Qwen2.5-MM-v1.2.9-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 URajinda/ShweYon_Qwen2.5-MM-v1.2.9-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 URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

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 URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

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 URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

Use Docker

docker model run hf.co/URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

LM Studio
Jan
Ollama
How to use URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF with Ollama:
```
ollama run hf.co/URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16
```
Unsloth Desktop

How to use URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

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": "URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF with Docker Model Runner:
```
docker model run hf.co/URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16
```

Lemonade

How to use URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

Run and chat with the model

lemonade run user.ShweYon_Qwen2.5-MM-v1.2.9-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use URajinda/ShweYon_Qwen2.5-MM-v1.2.9-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 URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

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 URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16

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 "URajinda/ShweYon_Qwen2.5-MM-v1.2.9-GGUF:F16" \
  --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"

🐰 ShweYon Qwen2.5-MM-v1.2.9 GGUF

ဤသည်မှာ URajinda/ShweYon_Qwen2.5-MM-v1.2.9-Final-Manual-Fixed ၏ တရားဝင် GGUF Version ဖြစ်ပါသည်။ ဤမော်ဒယ်ကို llama.cpp အသုံးပြု၍ မြန်မာဘာသာစကား၏ ဝိသေသလက္ခဏာများ မပျောက်ပျက်စေရန် Manual အချောသပ် Quantize လုပ်ထားခြင်း ဖြစ်ပါသည်။

📂 Available Formats

ShweYon_v1.2.9_F16.gguf (3.1 GB) - အရည်အသွေး အကောင်းဆုံး (Original Precision)။ ကျန်ရှိသော Format များကို ဤဖိုင်မှတစ်ဆင့် ထပ်မံ Quantize လုပ်နိုင်ပါသည်။
ShweYon_v1.2.9_Q4_K_M.gguf (1.0 GB) - ဖုန်းများနှင့် PC များတွင် အသုံးပြုရန် အမျှတဆုံး Version (Fast & Smart)။

🛠 How to Use (အသုံးပြုနည်း)

1. LM Studio / PC

Search box တွင် ShweYon ဟု ရှာဖွေပါ သို့မဟုတ် ဤဖိုင်ကို Download ဆွဲ၍ Models folder ထဲသို့ ထည့်ပါ။
System Prompt: သင်သည် ယဉ်ကျေးပျူငှာသော မြန်မာ AI 'ရွှေယုန်' ဖြစ်သည်။ ဟု ထည့်သွင်းပေးပါ။
Settings: Temperature: 0.3 နှင့် Repeat Penalty: 1.1 ကို အသုံးပြုရန် အကြံပြုပါသည်။

2. Ollama

အောက်ပါအတိုင်း Modelfile ကို တည်ဆောက်၍ အသုံးပြုနိုင်ပါသည်။

FROM ./ShweYon_v1.2.9_Q4_K_M.gguf
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"

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GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

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