FIM-8B
📄 Paper · 💻 GitHub · 🤗 Dataset · 🤗 Collection
FIM-8B is the strongest released model of "Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models": Qwen3-8B, mid-trained on function-aware FIM data, then post-trained on SWE-Lego agent trajectories. The mid-training stage is the only difference from a standard SWE-Lego reproduction — worth +3.2 points on SWE-Bench-Verified and +5.4 on SWE-Bench-Lite. Unlike FIM-7B and FIM-14B (R2E-Gym scaffold), this model is evaluated with the SWE-Lego setup: OpenHands CodeActAgent for inference and the official SWE-bench harness for scoring.
Training pipeline
- Base model:
Qwen/Qwen3-8B - FIM mid-training:
midtraining/configs/fim_midtrain.yamlon TIGER-Lab/FIM-Midtraining-400K (as-run copy:FIM_Midtrain_8B.yaml) → intermediate checkpoint released as TIGER-Lab/FIM-Mid-8B - Post-training: SFT on SWE-Lego trajectories (real + synthetic,
resolved-filtered, 2 epochs) —posttraining/swe_lego/(as-run copy:FIM_Posttrain_8B.yaml)
Results
Means over three evaluation seeds, identical harness for both arms (paper Table 1):
| Setting | SWE-Bench-Verified | SWE-Bench-Lite |
|---|---|---|
| Qwen3-8B + SWE-Lego (reproduced) | 31.80 | 27.30 |
| FIM-8B (+ FIM mid-training) | 35.00 | 32.70 |
| Δ | +3.20 | +5.40 |
Evaluate on SWE-Bench Verified
The complete pinned walkthrough lives at evaluation/swebench/released_checkpoints.md.
1. Serve the model with vLLM
The checkpoint ships max_position_embeddings: 163840 and its own chat template, so no rope or template overrides are needed:
CUDA_VISIBLE_DEVICES=0 \
python -m vllm.entrypoints.openai.api_server \
--model TIGER-Lab/FIM-8B \
--served-model-name FIM-8B \
--host 127.0.0.1 \
--port 8400 \
--tensor-parallel-size 1 \
--max-model-len 163840 \
--max-num-seqs 16 \
--gpu-memory-utilization 0.9 \
> vllm_fim8b.log 2>&1 &
Wait until the server is up (model load takes ~1 minute):
curl -s http://127.0.0.1:8400/v1/models
2. Run the agent on SWE-Bench Verified
Inference uses OpenHands 0.53.0 with CodeActAgent. Define the LLM in config.toml:
[llm.eval_fim]
model = "openai/FIM-8B"
base_url = "http://127.0.0.1:8400/v1"
api_key = "EMPTY"
temperature = 0.0
max_input_tokens = 147456
max_output_tokens = 16384
native_tool_calling = false
From the OpenHands checkout:
env USE_HINT_TEXT=false \
INSTRUCTION_TEMPLATE_NAME=swe_default.j2 \
ENABLE_PLAN_MODE=false \
ADD_IN_CONTEXT_LEARNING_EXAMPLE=false \
poetry run python evaluation/benchmarks/swe_bench/run_infer.py \
--config-file config.toml \
--agent-cls CodeActAgent \
--llm-config llm.eval_fim \
--max-iterations 100 \
--eval-num-workers 1 \
--eval-output-dir ./eval_out \
--dataset princeton-nlp/SWE-bench_Verified \
--split test \
--mode swe
For SWE-Bench Lite, use --dataset princeton-nlp/SWE-bench_Lite.
3. Score with the SWE-bench harness
Convert the OpenHands output.jsonl to a predictions file with evaluation/benchmarks/swe_bench/scripts/eval/convert_oh_output_to_swe_json.py, then evaluate it with the official SWE-bench harness (python -m swebench.harness.run_evaluation). The reported score is resolved_instances / total_instances.
Citation
@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}
}