Instructions to use scombat/s1d1-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scombat/s1d1-router with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scombat/s1d1-router")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scombat/s1d1-router") model = AutoModelForSequenceClassification.from_pretrained("scombat/s1d1-router", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tutorials/04-codex.md from scombat/s1d1-router: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
-
https://huggingface.co/scombat/s1d1-router/resolve/main/tutorials/04-codex.md
- Command line
-
hf download hf://scombat/s1d1-router/tutorials/04-codex.md
-
curl -L -o 04-codex.md https://huggingface.co/scombat/s1d1-router/resolve/main/tutorials/04-codex.md
Codex CLI
Facts from the Codex configuration reference (2026-10-15): config.toml keys model, model_provider,
[model_providers.<id>] (base_url, env_key, wire_api; responses is the only supported value),
model_reasoning_effort (values depend on the model: low, medium, high, xhigh, …), profile files
$CODEX_HOME/<name>.config.toml selected with --profile, and lifecycle hooks (features.hooks = true, event
UserPromptSubmit, command handlers). I did not find a documented way for a hook to change the model.
1. Choose at launch (router untested; -m and -c assumed from the Codex CLI)
python integrations/serve.py --model scombat/s1d1-router --policy integrations/policy.example.yaml &
python integrations/routed.py codex "fix the failing test in utils/"
runs codex -m <model> -c model_reasoning_effort="<effort>" "<prompt>". Edit the codex profile in
policy.example.yaml first: put the model ids of your account in models (they are placeholders) and keep only the
efforts your models accept. If -c is not available in your version, use profile files (one per tier) and
--profile.
2. Through a gateway
Point a custom provider at a LiteLLM proxy (base_url = "http://localhost:4000/v1", env_key), with the Responses
API enabled on the proxy, and use the hook of 02-litellm.md. Untested: the Responses path of the hook is not
written; check that LiteLLM runs async_pre_call_hook on /v1/responses for your version.