Instructions to use feedseawave/WeDLM-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use feedseawave/WeDLM-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="feedseawave/WeDLM-8B-Instruct-GGUF",
	filename="WeDLM-8B-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use feedseawave/WeDLM-8B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf feedseawave/WeDLM-8B-Instruct-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 feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf feedseawave/WeDLM-8B-Instruct-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 feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use feedseawave/WeDLM-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "feedseawave/WeDLM-8B-Instruct-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": "feedseawave/WeDLM-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M

Ollama
How to use feedseawave/WeDLM-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use feedseawave/WeDLM-8B-Instruct-GGUF 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 feedseawave/WeDLM-8B-Instruct-GGUF 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 feedseawave/WeDLM-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for feedseawave/WeDLM-8B-Instruct-GGUF to start chatting

Pi new

How to use feedseawave/WeDLM-8B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M

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": "feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use feedseawave/WeDLM-8B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf feedseawave/WeDLM-8B-Instruct-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 feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use feedseawave/WeDLM-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use feedseawave/WeDLM-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull feedseawave/WeDLM-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WeDLM-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

WeDLM-8B-Instruct-GGUF

First GGUF quantization of Tencent WeDLM-8B-Instruct!

Quantized using llama.cpp b7688.

Original model: tencent/WeDLM-8B-Instruct

About

WeDLM is an 8B parameter instruction-tuned model by Tencent, supporting English and Chinese. It features QK Norm architecture similar to Qwen3.

This GGUF uses qwen3 architecture identifier for maximum llama.cpp compatibility.

Available Files

Filename	Quant	Size	Description
WeDLM-8B-Instruct-Q4_K_M.gguf	Q4_K_M	4.68 GB	Good quality, recommended for most use cases
WeDLM-8B-Instruct-Q8_0.gguf	Q8_0	8.11 GB	High quality, best accuracy

Performance Benchmarks

CPU (16 threads, Zen4)

Quant	Prompt Processing	Text Generation
Q4_K_M	88.65 t/s	8.27 t/s
Q8_0	50.80 t/s	5.17 t/s

GPU (RTX 4060 Laptop, 8GB VRAM)

Quant	Prompt Processing	Text Generation
Q4_K_M	1833.84 t/s	37.08 t/s

Q4_K_M recommended for RTX 4060 (fits in 8GB VRAM)

Prompt Format (ChatML)

<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant

Usage

llama.cpp

./llama-cli -m WeDLM-8B-Instruct-Q4_K_M.gguf \
  -p "<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n" \
  -n 256 -ngl 99

Ollama

# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./WeDLM-8B-Instruct-Q4_K_M.gguf
TEMPLATE "<|im_start|>user\n{{ .Prompt }}<|im_end|>\n<|im_start|>assistant\n"
EOF

ollama create wedlm -f Modelfile
ollama run wedlm

Hardware Requirements

Quant	Min VRAM	Recommended RAM
Q4_K_M	6 GB	8 GB
Q8_0	10 GB	12 GB

Model Architecture

Parameters: 8.19B
Layers: 36
Hidden Size: 4096
Attention Heads: 32 (8 KV heads, GQA)
Context Length: 16384
Features: QK Norm, SwiGLU, RoPE (theta=1M)

Acknowledgements

Original model: Tencent WeDLM Team
Inference framework: llama.cpp

Disclaimer

This is an unofficial quantization. For official support, please refer to the original model repository.

Downloads last month: 412

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

Model tree for feedseawave/WeDLM-8B-Instruct-GGUF

Base model

tencent/WeDLM-8B-Instruct

Quantized

(4)

this model