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TIGER-Lab
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Model Details
Model Provider
TIGER-Lab
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-8Bmidtraining/configs/fim_midtrain.yaml on TIGER-Lab/FIM-Midtraining-400K — AdamW, lr 1.0e-5, cosine schedule, warmup ratio 0.1, weight decay 0.05, one epoch, sequence length 32768, bf16 (as-run copy: FIM_Midtrain_8B.yaml)Ships the native Qwen3 40960 context (the yarn extension to 163840 was applied at post-training time); no overrides needed:
bash
CUDA_VISIBLE_DEVICES=0 \python -m vllm.entrypoints.openai.api_server \--model TIGER-Lab/FIM-Mid-8B \--served-model-name FIM-Mid-8B \--host 127.0.0.1 \--port 8400 \--tensor-parallel-size 1 \--max-model-len 40960 \--gpu-memory-utilization 0.9 \> vllm_fim_mid8b.log 2>&1 &
To reproduce FIM-8B, run SWE-Lego trajectory SFT from this checkpoint — the exact config is posttraining/swe_lego/FIM_Posttrain_8B.yaml (LLaMA-Factory, full fine-tuning, lr 1.0e-4, 2 epochs — the official SWE-Lego recipe's 4 overfits this base — cutoff 131072 with yarn rope scaling, qwen3_nothink template, turn_mask enabled), which already points at this repo id. See posttraining/swe_lego/ for the walkthrough.
bibtex
@article{wang2026fim,title={Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models},author={Wang, Yubo and Liang, Jiarong and Zhang, Yuxuan and Liu, Xuye and Wei, Cong and Zhang, Yuyu and Nie, Ping and Chen, Wenhu},journal={arXiv preprint arXiv:2607.12463},year={2026}}