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Model: xiaoyuchen1/societas-router-lenv3-17b
Source: Original Platform

README.md

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+---
+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