Text Classification
Transformers
Safetensors
multilingual
modernbert
routing
llm-routing
prompt-classification
complexity-estimation
text-embeddings-inference
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
File size: 2,889 Bytes
7783834 | 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 51 | # Claude Code
Facts from the Claude Code documentation (2026-10-15):
- Model: `--model`, `/model`, `ANTHROPIC_MODEL`, `model` in settings; aliases `haiku`, `sonnet`, `opus`, `fable`,
`opusplan` (Opus in plan mode, Sonnet to execute); `CLAUDE_CODE_SUBAGENT_MODEL`; a sub-agent can pin `model` and
`effort` in its frontmatter.
- Effort: `--effort`, `/effort`, `CLAUDE_CODE_EFFORT_LEVEL`, `effortLevel` in settings; levels `low`, `medium`, `high`,
`xhigh`, `max` (`max` not accepted in `effortLevel`); `ultrathink` in a prompt raises depth for one turn.
- Gateway: `ANTHROPIC_BASE_URL` + `ANTHROPIC_AUTH_TOKEN`. **Anthropic does not support routing Claude Code to
non-Claude models through a gateway**: keep the pool to Claude models.
- With `CLAUDE_CODE_GATEWAY_HINT_HEADERS=1` (v2.1.273+) a custom gateway receives `x-claude-code-session-id`,
`x-claude-code-prompt-id` (one id per user prompt), `x-claude-code-request-class` (`main`, `subagent`, `workflow`,
`compaction`, `auxiliary`).
## 1. Simplest: choose at launch (*documented flags, router untested*)
```bash
python integrations/serve.py --model scombat/s1d1-router --policy integrations/policy.example.yaml &
python integrations/routed.py claude "refactor the parser and add tests"
```
`routed.py` asks the router about your first prompt and starts `claude --model <model> --effort <effort> "<prompt>"`.
The session keeps that model; use `/model` and `/effort` to change it. Nothing is intercepted.
## 2. Per request, through a gateway (*untested end to end*)
```bash
export ANTHROPIC_BASE_URL=http://localhost:4000
export ANTHROPIC_AUTH_TOKEN=<litellm key>
export CLAUDE_CODE_GATEWAY_HINT_HEADERS=1
claude --model s1d1-auto
```
with the LiteLLM hook of `02-litellm.md` (A). The hook routes on the last human text of the request (tool results are
skipped), remembers one decision per `x-claude-code-prompt-id`, and leaves `auxiliary` requests alone.
Cautions from the gateway guide:
- keep forwarding `anthropic-beta`/`anthropic-version` and the `system` array unchanged; do not rewrite bodies except
`model` and the effort field;
- changing the model between turns can make the API reject old thinking blocks ("bound to a different conversation");
Claude Code then drops earlier thinking and retries, at the price of the cache. This is why the policy is
escalate-only per session;
- a rejected `output_config.effort` is retried without it by Claude Code.
## 3. No router at all: the same policy as static settings
```json
{
"model": "sonnet",
"effortLevel": "medium",
"availableModels": ["haiku", "sonnet", "opus", "fable"]
}
```
plus `CLAUDE_CODE_SUBAGENT_MODEL=haiku` for exploration sub-agents, a sub-agent frontmatter with `model: haiku` and
`effort: low` for cheap searches, and `opusplan` to plan with Opus and execute with Sonnet. The router replaces these
fixed choices with a choice per prompt.
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