Instructions to use kanaton/WebCoder-T28g-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 kanaton/WebCoder-T28g-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 kanaton/WebCoder-T28g-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kanaton/WebCoder-T28g-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 kanaton/WebCoder-T28g-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kanaton/WebCoder-T28g-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 kanaton/WebCoder-T28g-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kanaton/WebCoder-T28g-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 kanaton/WebCoder-T28g-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kanaton/WebCoder-T28g-GGUF:Q4_K_M

Use Docker

docker model run hf.co/kanaton/WebCoder-T28g-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use kanaton/WebCoder-T28g-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kanaton/WebCoder-T28g-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": "kanaton/WebCoder-T28g-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kanaton/WebCoder-T28g-GGUF:Q4_K_M

Ollama
How to use kanaton/WebCoder-T28g-GGUF with Ollama:
```
ollama run hf.co/kanaton/WebCoder-T28g-GGUF:Q4_K_M
```
Unsloth Desktop

How to use kanaton/WebCoder-T28g-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kanaton/WebCoder-T28g-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": "kanaton/WebCoder-T28g-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kanaton/WebCoder-T28g-GGUF with Docker Model Runner:
```
docker model run hf.co/kanaton/WebCoder-T28g-GGUF:Q4_K_M
```

Lemonade

How to use kanaton/WebCoder-T28g-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kanaton/WebCoder-T28g-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WebCoder-T28g-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use kanaton/WebCoder-T28g-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 kanaton/WebCoder-T28g-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 kanaton/WebCoder-T28g-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kanaton/WebCoder-T28g-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kanaton/WebCoder-T28g-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 "kanaton/WebCoder-T28g-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"

WebCoder-T28g-GGUF

File size: 1,695 Bytes

e5f8ba6

{%- if messages[0]['role'] == 'system' %}
    {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
    {{- '<|im_start|>system\nTu es WebCoder, un assistant de developpement web cree par Pierre Jean Duvivier.<|im_end|>\n' }}
{%- endif %}
{%- for message in messages %}
    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
    {%- elif message.role == "assistant" %}
        {{- '<|im_start|>' + message.role }}
        {%- if message.content %}
            {{- '\n' + message.content }}
        {%- endif %}
        {%- for tool_call in message.tool_calls %}
            {%- if tool_call.function is defined %}
                {%- set tool_call = tool_call.function %}
            {%- endif %}
            {{- '\n<tool_call>\n{"name": "' }}
            {{- tool_call.name }}
            {{- '", "arguments": ' }}
            {{- tool_call.arguments | tojson }}
            {{- '}\n</tool_call>' }}
        {%- endfor %}
        {{- '<|im_end|>\n' }}
    {%- elif message.role == "tool" %}
        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
            {{- '<|im_start|>user' }}
        {%- endif %}
        {{- '\n<tool_response>\n' }}
        {{- message.content }}
        {{- '\n</tool_response>' }}
        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
            {{- '<|im_end|>\n' }}
        {%- endif %}
    {%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n' }}
{%- endif %}