Instructions to use kanaton/webcoder-t28n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kanaton/webcoder-t28n with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
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
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 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
- Llama new
- 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
- Pi
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"
File size: 2,519 Bytes
4d97a01 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | # 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.
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