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"
Upload THIRD_PARTY_NOTICES.md with huggingface_hub
Browse files- THIRD_PARTY_NOTICES.md +49 -0
THIRD_PARTY_NOTICES.md
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# Third-party notices
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Généré le 2026-10-12 (P01 / T04). Ce fichier recense les composants tiers réellement
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utilisés par le pipeline, avec leur licence déclarée. Les révisions des modèles sont
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figées dans `reports/models.lock.json` ; les versions des paquets dans `requirements.lock`.
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Aucun poids n'a été redistribué ni publié. Aucune donnée privée n'a été transmise.
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## Modèles
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| Composant | Rôle | Révision (sha) | Licence | Usage commercial |
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|---|---|---|---|---|
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| `Qwen/Qwen2.5-Coder-1.5B-Instruct` | Élève par défaut | `2e1fd397ee46e1388853d2af2c993145b0f1098a` | Apache-2.0 | Autorisé |
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| `Qwen/Qwen2.5-Coder-7B-Instruct` | Professeur local (candidat) | `c03e6d358207e414f1eca0bb1891e29f1db0e242` | Apache-2.0 | Autorisé (professeur non déployé sur la cible) |
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| `Qwen/Qwen2.5-Coder-3B-Instruct` | **Exclu par défaut** | `488639f1ff808d1d3d0ba301aef8c11461451ec5` | Qwen Research License (`license:other`) | **Restreint** — exclu sans autorisation explicite (`configs/project.json`) |
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Vérification de la licence de l'élève : fichier `LICENSE` téléchargé et inspecté →
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« Apache License, Version 2.0, January 2004 » (copie conservée dans `reports/student_LICENSE.txt`).
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## Paquets Python (versions figées, `requirements.lock`)
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| Paquet | Version | Licence (déclarée par le projet amont) |
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|---|---|---|
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| torch | 2.14.1+cpu | BSD-3-Clause |
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| transformers | 5.19.0 | Apache-2.0 |
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| peft | 0.21.2 | Apache-2.0 |
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| trl | 1.15.0 | Apache-2.0 |
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| datasets | 5.1.0 | Apache-2.0 |
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| accelerate | 1.15.0 | Apache-2.0 |
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| safetensors | 0.8.0 | Apache-2.0 |
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| huggingface_hub | 2.2.0 | Apache-2.0 |
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| tokenizers | 0.23.3 | Apache-2.0 |
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| numpy | 2.5.4 | BSD-3-Clause |
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## Outils divers (hors venv Python)
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- Git, PHP 8.2.12, Node v24.21.0, npm 11.8.0, Composer 2.8.1 — licences respectives de leurs projets.
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- **llama.cpp** : **installé et utilisé** (build CPU-only, **commit `77757a34672a888845a09ab45728b8d34aa475ff`**, licence **MIT**) — fournit `llama-server`, `llama-quantize`, `llama-bench`.
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## Nom servi (alias)
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Le worker est exposé sous le nom **`webcoder`** (`--alias`, **D-062**). C'est un **alias cosmétique** :
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aucun poids tiers n'est renommé, modifié, republié ni redistribué. La base réelle reste
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`Qwen/Qwen2.5-Coder-7B-Instruct` (**Apache-2.0**).
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## Rappel
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La disponibilité publique du code ou des poids ne vaut pas autorisation de réutilisation :
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le corpus de formation (P04) exigera son propre inventaire de provenance et de droits.
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