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Model: TIGER-Lab/FIM-8B
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-8B
+datasets:
+- TIGER-Lab/FIM-Midtraining-400K
+- SWE-Lego/SWE-Lego-Synthetic-Data
+tags:
+- code
+- software-engineering
+- agent
+---
+
+# FIM-8B
+
+[📄 Paper](https://arxiv.org/abs/2607.12463) · [💻 GitHub](https://github.com/TIGER-AI-Lab/FIM-Midtraining) · [🤗 Dataset](https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K) · [🤗 Collection](https://huggingface.co/collections/TIGER-Lab/fim-midtraining)
+
+**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`](https://huggingface.co/Qwen/Qwen3-8B)
+- **FIM mid-training**: [`midtraining/configs/fim_midtrain.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/midtraining/configs/fim_midtrain.yaml) on [TIGER-Lab/FIM-Midtraining-400K](https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K) (as-run copy: [`FIM_Midtrain_8B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/midtraining/configs/FIM_Midtrain_8B.yaml)) → intermediate checkpoint released as [TIGER-Lab/FIM-Mid-8B](https://huggingface.co/TIGER-Lab/FIM-Mid-8B)
+- **Post-training**: SFT on SWE-Lego trajectories (real + synthetic, `resolved`-filtered, 2 epochs) — [`posttraining/swe_lego/`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/tree/main/posttraining/swe_lego) (as-run copy: [`FIM_Posttrain_8B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/posttraining/swe_lego/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`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/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:
+
+```bash
+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):
+
+```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 with `CodeActAgent`. 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`.
+
+## Citation
+
+```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}
+}
+```

chat_template.jinja

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+{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system
+' + system_message + '<|im_end|>
+' }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user
+' + content + '<|im_end|>
+<|im_start|>assistant
+' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>
+' }}{% endif %}{% endfor %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 163840.0,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "factor": 4.0,
+    "original_max_position_embeddings": 40960,
+    "rope_theta": 1000000,
+    "rope_type": "yarn"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.0.0"
+}

model.safetensors

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tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "model_max_length": 131072,
+  "model_specific_special_tokens": {},
+  "pad_token": "<|endoftext|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}