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

Libraries

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

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

pipe = pipeline("text-generation", model="TIGER-Lab/FIM-Mid-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-Mid-14B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/FIM-Mid-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-Mid-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-Mid-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-Mid-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

FIM-Mid-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
	tags:
	- code
	- software-engineering
	- fim
	---

	# FIM-Mid-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-Mid-14B is the mid-trained checkpoint of the FIM 14B pipeline: `Qwen2.5-Coder-14B-Instruct` after function-aware FIM mid-training, before agent post-training. Post-training this checkpoint on R2E-Gym agent trajectories produces [TIGER-Lab/FIM-14B](https://huggingface.co/TIGER-Lab/FIM-14B).

	It is released for reproducibility and further post-training. The paper deliberately never scores mid-training-only checkpoints — a FIM-only model has degraded instruction-following and cannot be compared fairly against instruction-tuned baselines; every reported gain is one that survives post-training.

	## Training

	- 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) — AdamW, lr `1.0e-5`, cosine schedule, warmup ratio `0.1`, weight decay `0.05`, one epoch, sequence length `32768`, bf16 (as-run copy: [`FIM_Midtrain_14B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/midtraining/configs/FIM_Midtrain_14B.yaml))
	- Post-training: none — see [TIGER-Lab/FIM-14B](https://huggingface.co/TIGER-Lab/FIM-14B) for the post-trained agent model

	## Serve with vLLM

	A standard Qwen2.5 checkpoint; no overrides needed at its native 32768 context:

	```bash
	CUDA_VISIBLE_DEVICES=0 \
	python -m vllm.entrypoints.openai.api_server \
	--model TIGER-Lab/FIM-Mid-14B \
	--served-model-name FIM-Mid-14B \
	--host 127.0.0.1 \
	--port 8400 \
	--tensor-parallel-size 1 \
	--max-model-len 32768 \
	--gpu-memory-utilization 0.9 \
	> vllm_fim_mid14b.log 2>&1 &
	```

	## Post-training

	To reproduce FIM-14B, run R2E-Gym trajectory SFT from this checkpoint — the exact config is [`posttraining/r2egym/FIM_Posttrain_14B.yaml`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/blob/main/posttraining/r2egym/FIM_Posttrain_14B.yaml) (LLaMA-Factory, full fine-tuning, lr `1.0e-5`, 2 epochs, cutoff 32768), which already points at this repo id. See [`posttraining/r2egym/`](https://github.com/TIGER-AI-Lab/FIM-Midtraining/tree/main/posttraining/r2egym) for the walkthrough.

	## 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}
	}
	```