Instructions to use TIGER-Lab/FIM-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/FIM-14B with Transformers:

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

pipe = pipeline("text-generation", model="TIGER-Lab/FIM-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/FIM-14B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/FIM-14B")
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-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/FIM-14B"
# 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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/FIM-14B

SGLang

How to use TIGER-Lab/FIM-14B 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-14B" \
    --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-14B",
		"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-14B" \
        --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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/FIM-14B with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/FIM-14B
```

FIM-14B / README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model:
	- Qwen/Qwen2.5-Coder-14B-Instruct
	datasets:
	- TIGER-Lab/FIM-Midtraining-400K
	- R2E-Gym/R2EGym-SFT-Trajectories
	tags:
	- code
	- software-engineering
	- agent
	---

	# FIM-14B

	[📄 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-14B is the 14B coding-agent model of "Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models": `Qwen2.5-Coder-14B-Instruct`, mid-trained on function-aware FIM data, then post-trained on R2E-Gym agent trajectories with the upstream recipe unmodified. The mid-training stage is the only difference from a standard R2E-Gym reproduction — worth +3.0 points on SWE-Bench-Verified and +4.0 on SWE-Bench-Lite, while also recovering most of the general-capability erosion that agentic post-training inflicts (LiveCodeBench +11.1, τ-bench +3.9, BFCL +2.4 over the post-training-only arm).

	## Training pipeline

	- Base model: [`Qwen/Qwen2.5-Coder-14B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
	- 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_14B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/midtraining/configs/FIM_Midtrain_14B.yaml)) → intermediate checkpoint released as [TIGER-Lab/FIM-Mid-14B](https://huggingface.co/TIGER-Lab/FIM-Mid-14B)
	- Post-training: SFT on R2E-Gym agent trajectories — [`posttraining/r2egym/`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/tree/main/posttraining/r2egym) (as-run copy: [`FIM_Posttrain_14B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/posttraining/r2egym/FIM_Posttrain_14B.yaml))

	## Results

	Means over three evaluation seeds, identical harness for both arms (paper Tables 1–2):

	\| Setting \| SWE-Bench-Verified \| SWE-Bench-Lite \|
	\|---\|---\|---\|
	\| Qwen2.5-Coder-14B-Instruct + R2E-Gym (reproduced) \| 26.20 \| 18.00 \|
	\| FIM-14B (+ FIM mid-training) \| 29.20 \| 22.00 \|
	\| Δ \| +3.00 \| +4.00 \|

	Capability preservation at 14B (six benchmarks outside SWE-Bench):

	\| Setting \| LiveCode \| OJBench \| FSB-EN \| Terminal \| τ-bench \| BFCL \| Avg \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| + R2E-Gym only \| 24.10 \| 2.80 \| 47.72 \| 2.41 \| 3.40 \| 15.80 \| 16.04 \|
	\| FIM-14B \| 35.20 \| 4.74 \| 48.25 \| 3.66 \| 7.30 \| 18.20 \| 19.56 \|

	Reproduction guides for all six: [`evaluation/`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/tree/main/evaluation).

	## Evaluate on SWE-Bench Verified

	FIM-14B 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`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/evaluation/swebench/released_checkpoints.md).

	### 1. Serve the model with vLLM

	```bash
	CUDA_VISIBLE_DEVICES=0 \
	VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
	python -m vllm.entrypoints.openai.api_server \
	--model TIGER-Lab/FIM-14B \
	--served-model-name FIM-14B \
	--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_fim14b.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

	From an upstream, unmodified [R2E-Gym](https://github.com/R2E-Gym/R2E-Gym) checkout (Docker required):

	```bash
	export OPENAI_API_KEY=EMPTY
	export 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-14B_swebench_verified_r1" \
	--llm_name "openai/FIM-14B" \
	--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
	```

	For SWE-Bench Lite, use `--dataset "R2E-Gym/SWE-Bench-Lite" --k 300`.

	### 3. Score with the official SWE-bench harness

	Convert the trajectories to a submission and score with the official harness — [`evaluation/swebench/score.sh`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/evaluation/swebench/score.sh). The reported number 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}
	}
	```