---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen3-8B
tags:
- code
- software-engineering
- agent
---

# FIM-8B Inference on SWE-Bench Verified

This guide describes how to run the **FIM-8B** checkpoint on SWE-Bench Verified (and Lite). Unlike FIM-7B and FIM-14B (R2E-Gym scaffold), this model is post-trained on SWE-Lego trajectories and is evaluated with the SWE-Lego setup: OpenHands `CodeActAgent` for inference and the official SWE-bench harness for scoring.

## Model

Local path: `models/FIM-8B/` (checkpoints are gitignored; do not commit them).

- Base model: `Qwen/Qwen3-8B`
- FIM mid-training: FIM v2/v3 data
- Post-training: SFT on SWE-Lego trajectories


## 1. Serve the model with vLLM

```bash
CUDA_VISIBLE_DEVICES=0 \
python -m vllm.entrypoints.openai.api_server \
  --model models/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 &
```

The checkpoint ships `max_position_embeddings: 163840` and its own chat template, so no rope or template overrides are needed.

Wait until the server is up (model load takes ~1 minute):

```bash
curl -s http://127.0.0.1:8400/v1/models
```

## 2. Run the agent on SWE-Bench Verified

Inference uses OpenHands 0.53.0. Define the LLM in `config.toml`:

```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:

```bash
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`.