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TIGER-Lab
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Model Details
Model Provider
TIGER-Lab
Model Tree
Qwen/Qwen2.5-Coder-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen2.5-Coder-7B-Instructmidtraining/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_7B.yaml)A standard Qwen2.5 checkpoint; no overrides needed at its native 32768 context:
bash
CUDA_VISIBLE_DEVICES=0 \python -m vllm.entrypoints.openai.api_server \--model TIGER-Lab/FIM-Mid-7B \--served-model-name FIM-Mid-7B \--host 127.0.0.1 \--port 8400 \--tensor-parallel-size 1 \--max-model-len 32768 \--gpu-memory-utilization 0.9 \> vllm_fim_mid7b.log 2>&1 &
To reproduce FIM-7B, run R2E-Gym trajectory SFT from this checkpoint — the exact config is posttraining/r2egym/FIM_Posttrain_7B.yaml (LLaMA-Factory, full fine-tuning, lr 1.0e-5, 2 epochs, cutoff 32768), which already points at this repo id. See posttraining/r2egym/ 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}}