Instructions to use kanaton/webcoder-t28n 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-t28n 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-t28n:Q8_0
# Run inference directly in the terminal:
llama cli -hf kanaton/webcoder-t28n:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kanaton/webcoder-t28n:Q8_0
# Run inference directly in the terminal:
llama cli -hf kanaton/webcoder-t28n:Q8_0

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-t28n:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf kanaton/webcoder-t28n:Q8_0

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-t28n:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kanaton/webcoder-t28n:Q8_0

Use Docker

docker model run hf.co/kanaton/webcoder-t28n:Q8_0

LM Studio
Jan

vLLM

How to use kanaton/webcoder-t28n with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kanaton/webcoder-t28n"
# 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-t28n",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kanaton/webcoder-t28n:Q8_0

Ollama
How to use kanaton/webcoder-t28n with Ollama:
```
ollama run hf.co/kanaton/webcoder-t28n:Q8_0
```
Unsloth Desktop

How to use kanaton/webcoder-t28n 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-t28n:Q8_0

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-t28n:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kanaton/webcoder-t28n with Docker Model Runner:
```
docker model run hf.co/kanaton/webcoder-t28n:Q8_0
```

Lemonade

How to use kanaton/webcoder-t28n with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kanaton/webcoder-t28n:Q8_0

Run and chat with the model

lemonade run user.webcoder-t28n-Q8_0

List all available models

lemonade list

Hermes Agent

How to use kanaton/webcoder-t28n 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-t28n:Q8_0

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-t28n:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kanaton/webcoder-t28n 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-t28n:Q8_0

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-t28n:Q8_0" \
  --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-t28n / THIRD_PARTY_NOTICES.md

kanaton

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Third-party notices

Généré le 2026-10-12 (P01 / T04). Ce fichier recense les composants tiers réellement utilisés par le pipeline, avec leur licence déclarée. Les révisions des modèles sont figées dans reports/models.lock.json ; les versions des paquets dans requirements.lock.

Aucun poids n'a été redistribué ni publié. Aucune donnée privée n'a été transmise.

Modèles

Composant	Rôle	Révision (sha)	Licence	Usage commercial
`Qwen/Qwen2.5-Coder-1.5B-Instruct`	Élève par défaut	`2e1fd397ee46e1388853d2af2c993145b0f1098a`	Apache-2.0	Autorisé
`Qwen/Qwen2.5-Coder-7B-Instruct`	Professeur local (candidat)	`c03e6d358207e414f1eca0bb1891e29f1db0e242`	Apache-2.0	Autorisé (professeur non déployé sur la cible)
`Qwen/Qwen2.5-Coder-3B-Instruct`	Exclu par défaut	`488639f1ff808d1d3d0ba301aef8c11461451ec5`	Qwen Research License (`license:other`)	Restreint — exclu sans autorisation explicite (`configs/project.json`)

Vérification de la licence de l'élève : fichier LICENSE téléchargé et inspecté → « Apache License, Version 2.0, January 2004 » (copie conservée dans reports/student_LICENSE.txt).

Paquets Python (versions figées, `requirements.lock`)

Paquet	Version	Licence (déclarée par le projet amont)
torch	2.14.1+cpu	BSD-3-Clause
transformers	5.19.0	Apache-2.0
peft	0.21.2	Apache-2.0
trl	1.15.0	Apache-2.0
datasets	5.1.0	Apache-2.0
accelerate	1.15.0	Apache-2.0
safetensors	0.8.0	Apache-2.0
huggingface_hub	2.2.0	Apache-2.0
tokenizers	0.23.3	Apache-2.0
numpy	2.5.4	BSD-3-Clause

Outils divers (hors venv Python)

Git, PHP 8.2.12, Node v24.21.0, npm 11.8.0, Composer 2.8.1 — licences respectives de leurs projets.
llama.cpp : installé et utilisé (build CPU-only, commit 77757a34672a888845a09ab45728b8d34aa475ff, licence MIT) — fournit llama-server, llama-quantize, llama-bench.

Nom servi (alias)

Le worker est exposé sous le nom webcoder (--alias, D-062). C'est un alias cosmétique : aucun poids tiers n'est renommé, modifié, republié ni redistribué. La base réelle reste Qwen/Qwen2.5-Coder-7B-Instruct (Apache-2.0).

Rappel

La disponibilité publique du code ou des poids ne vaut pas autorisation de réutilisation : le corpus de formation (P04) exigera son propre inventaire de provenance et de droits.

Third-party notices

Modèles

Paquets Python (versions figées, requirements.lock)

Outils divers (hors venv Python)

Nom servi (alias)

Rappel

Paquets Python (versions figées, `requirements.lock`)