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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 (as-run copy: FIM_Midtrain_7B.yaml) → intermediate checkpoint released as TIGER-Lab/FIM-Mid-7Bposttraining/r2egym/ (as-run copy: FIM_Posttrain_7B.yaml)Means over three evaluation seeds, identical harness for both arms (paper Table 1):
| Setting | SWE-Bench-Verified | SWE-Bench-Lite |
|---|---|---|
| Qwen2.5-Coder-7B-Instruct + R2E-Gym (reproduced) | 15.00 | 11.33 |
| FIM-7B (+ FIM mid-training) | 17.80 | 15.00 |
| Δ | +2.80 | +3.67 |
FIM-7B is evaluated with the R2E-Gym agent scaffold (fixed by its post-training pipeline). The complete pinned walkthrough lives at evaluation/swebench/released_checkpoints.md.
bash
CUDA_VISIBLE_DEVICES=0 \VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \python -m vllm.entrypoints.openai.api_server \ --model TIGER-Lab/FIM-7B \ --served-model-name FIM-7B \ --host 127.0.0.1 \ --port 8400 \ --tensor-parallel-size 1 \ --max-model-len 65536 \ --hf-overrides '{"max_position_embeddings": 65536}' \ --enable-prefix-caching \ --gpu-memory-utilization 0.9 \ > vllm_fim7b.log 2>&1 &Wait until the server is up (model load takes ~1 minute):
bash
curl -s http://127.0.0.1:8400/v1/modelsFrom an upstream, unmodified R2E-Gym checkout (Docker required):
bash
export OPENAI_API_KEY=EMPTYexport LLM_BASE_URL="http://127.0.0.1:8400/v1" uv run python src/r2egym/agenthub/run/edit.py runagent_multiple \ --dataset "R2E-Gym/SWE-Bench-Verified" \ --split "test" \ --start_idx 0 \ --k 500 \ --traj_dir "./traj" \ --exp_name "FIM-7B_swebench_verified_r1" \ --llm_name "openai/FIM-7B" \ --scaffold "r2egym" \ --backend "docker" \ --use_fn_calling False \ --temperature 0 \ --max_steps 40 \ --max_steps_absolute 100 \ --max_workers 6 \ --max_reward_calc_time 1200 \ --max_tokens 65536 \ --use_existing TrueFor SWE-Bench Lite, use --dataset "R2E-Gym/SWE-Bench-Lite" --k 300.
Convert the trajectories to a submission and score with the official harness — evaluation/swebench/score.sh. The reported number is resolved_instances / total_instances.
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}}CUDA_VISIBLE_DEVICES=0 \VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \python -m vllm.entrypoints.openai.api_server \ --model TIGER-Lab/FIM-7B \ --served-model-name FIM-7B \ --host 127.0.0.1 \ --port 8400 \ --tensor-parallel-size 1 \ --max-model-len 65536 \ --hf-overrides '{"max_position_embeddings": 65536}' \ --enable-prefix-caching \ --gpu-memory-utilization 0.9 \ > vllm_fim7b.log 2>&1 &export OPENAI_API_KEY=EMPTYexport LLM_BASE_URL="http://127.0.0.1:8400/v1" uv run python src/r2egym/agenthub/run/edit.py runagent_multiple \ --dataset "R2E-Gym/SWE-Bench-Verified" \ --split "test" \ --start_idx 0 \ --k 500 \ --traj_dir "./traj" \ --exp_name "FIM-7B_swebench_verified_r1" \ --llm_name "openai/FIM-7B" \ --scaffold "r2egym" \ --backend "docker" \ --use_fn_calling False \ --temperature 0 \ --max_steps 40 \ --max_steps_absolute 100 \ --max_workers 6 \ --max_reward_calc_time 1200 \ --max_tokens 65536 \ --use_existing True@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}}