---
library_name: transformers
license: other
base_model: Qwen/Qwen3-1.7B
tags:
- llama-factory
- full
- generated_from_trainer
- routing
- classifier
model-index:
- name: societas-router-lenv3-17b
  results: []
---

# societas-router-lenv3-17b

A per-call **LLM routing classifier** — a full-parameter fine-tune of
[Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B). At each step of an agent's trajectory it
reads a read-only snapshot of the trajectory-so-far and predicts the capability **tier** the *next*
model call needs, emitting a single JSON verdict `{"tier": "...", "reason": "..."}` with
`tier ∈ {Routine, Medium, Advanced}`.

## Results (held-out test split, 251 examples)

- **Accuracy: 60.16%** (151/251), `parse_fail = 0`, no think-token leakage. (chance = 33%)
- Per-tier recall: Routine 58.3% · Medium 52.4% · Advanced 69.9%.

## Usage note (important)

Trained with the Qwen3 chat template at `enable_thinking=False` (the empty `<think></think>` block is
in the prompt; the JSON verdict is the target). **Serve it the same way** — render prompts with
`enable_thinking=False`.

## Training procedure

Full-parameter SFT via LLaMA-Factory, bf16, on 2× A800-80G.

- learning_rate: 2e-05, cosine schedule, warmup_ratio 0.03
- per_device_train_batch_size: 1, gradient_accumulation_steps: 8, total_train_batch_size: 16
- num_epochs: 3.0, cutoff_len (max context): 16384, packing: off
- optimizer: AdamW (torch), betas=(0.9,0.999), eps=1e-08

### Framework versions

- Transformers 4.53.x / Pytorch 2.5.1+cu121 / Datasets / Tokenizers