Instructions to use TIGER-Lab/FIM-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/FIM-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TIGER-Lab/FIM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/FIM-8B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/FIM-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TIGER-Lab/FIM-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/FIM-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/FIM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/FIM-8B

SGLang

How to use TIGER-Lab/FIM-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TIGER-Lab/FIM-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/FIM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TIGER-Lab/FIM-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/FIM-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/FIM-8B with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/FIM-8B
```

FIM-8B / 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}
	}
	```