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Lance1573
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Model Details
Model Provider
Lance1573
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
adapter_model.safetensors: LoRA adapter weights.adapter_config.json: PEFT adapter configuration with base model set to Qwen/Qwen3.5-0.8B.tokenizer.json, tokenizer_config.json, chat_template.jinja: tokenizer assets copied from the training export.training_config.json: compact training hyperparameters.eval_metrics.json: ID test metrics for this router.Qwen/Qwen3.5-0.8BLORA16320.0530.000210244483Evaluated on the CodeRouterBench ID test split (n=2919):
| metric | value |
|---|---|
| Avg performance | 0.474415 |
| Oracle performance | 0.570049 |
| Oracle gap | 0.095634 |
| Routing accuracy | 0.361425 |
| rAcc | 0.424460 |
| Strong model call rate | 0.373073 |
| Perf/cost ratio | 364.336998 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model = "Qwen/Qwen3.5-0.8B"adapter_id = "Lance1573/acrouter-qwen35-08b-router-lora" tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)base = AutoModelForCausalLM.from_pretrained( base_model, torch_dtype="auto", device_map="auto", trust_remote_code=True,)model = PeftModel.from_pretrained(base, adapter_id)model.eval()The adapter is intended for model-routing prompts from Agent-as-a-Router rather than general-purpose instruction following.
This is a task-specific router trained for selecting among the backend models listed above. It should not be interpreted as a general coding assistant. The adapter does not include private API keys, raw trajectories, optimizer states, or training checkpoints.
bibtex
@article{agent2026zhou, title = {Agent-as-a-Router: Agentic Model Routing for Coding Tasks}, author = {Pengfei Zhou, Zhiwei Tang, Yixing Ma, Jiasheng Tang, Yizeng Han, Zhenglin Wan, Fanqing Meng, Wei Wang, Bohan Zhuang, Wangbo Zhao, Yang You}, journal = {arXiv preprint arXiv:2606.22902}, year = {2026}, archivePrefix = {arXiv}, eprint = {2606.22902}, url = {https://arxiv.org/abs/2606.22902},}from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model = "Qwen/Qwen3.5-0.8B"adapter_id = "Lance1573/acrouter-qwen35-08b-router-lora" tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)base = AutoModelForCausalLM.from_pretrained( base_model, torch_dtype="auto", device_map="auto", trust_remote_code=True,)model = PeftModel.from_pretrained(base, adapter_id)model.eval()@article{agent2026zhou, title = {Agent-as-a-Router: Agentic Model Routing for Coding Tasks}, author = {Pengfei Zhou, Zhiwei Tang, Yixing Ma, Jiasheng Tang, Yizeng Han, Zhenglin Wan, Fanqing Meng, Wei Wang, Bohan Zhuang, Wangbo Zhao, Yang You}, journal = {arXiv preprint arXiv:2606.22902}, year = {2026}, archivePrefix = {arXiv}, eprint = {2606.22902}, url = {https://arxiv.org/abs/2606.22902},}