Instructions to use TIGER-Lab/FIM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/FIM-7B with Transformers:

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

pipe = pipeline("text-generation", model="TIGER-Lab/FIM-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TIGER-Lab/FIM-7B")
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/FIM-7B")
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-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/FIM-7B"
# 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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/FIM-7B

SGLang

How to use TIGER-Lab/FIM-7B 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-7B" \
    --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-7B",
		"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-7B" \
        --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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/FIM-7B with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/FIM-7B
```

FIM-7B Inference on SWE-Bench Verified

This guide describes how to run the FIM-7B checkpoint on SWE-Bench Verified (and Lite) with the R2E-Gym agent scaffold in this repository.

Model

Local path: models/FIM-7B/ (checkpoints are gitignored; do not commit them).

Base model: Qwen/Qwen2.5-Coder-7B-Instruct
FIM mid-training: train/FIM_Midtrain_7B.yaml
Post-training: SFT on R2E-Gym agent trajectories

1. Serve the model with vLLM

CUDA_VISIBLE_DEVICES=0 \
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
.venv-vllm/bin/python -m vllm.entrypoints.openai.api_server \
  --model models/FIM-7B \
  --served-model-name FIM-7B \
  --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_fim7b.log 2>&1 &

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

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

2. Run the agent on SWE-Bench Verified

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-7B_swebench_verified_r1" \
  --llm_name "openai/FIM-7B" \
  --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