Instructions to use bloomer010/Ling-3.0-flash-REAP384-97B-A5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bloomer010/Ling-3.0-flash-REAP384-97B-A5B with Transformers:

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

pipe = pipeline("text-generation", model="bloomer010/Ling-3.0-flash-REAP384-97B-A5B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bloomer010/Ling-3.0-flash-REAP384-97B-A5B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bloomer010/Ling-3.0-flash-REAP384-97B-A5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bloomer010/Ling-3.0-flash-REAP384-97B-A5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bloomer010/Ling-3.0-flash-REAP384-97B-A5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bloomer010/Ling-3.0-flash-REAP384-97B-A5B

SGLang

How to use bloomer010/Ling-3.0-flash-REAP384-97B-A5B 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 "bloomer010/Ling-3.0-flash-REAP384-97B-A5B" \
    --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": "bloomer010/Ling-3.0-flash-REAP384-97B-A5B",
		"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 "bloomer010/Ling-3.0-flash-REAP384-97B-A5B" \
        --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": "bloomer010/Ling-3.0-flash-REAP384-97B-A5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bloomer010/Ling-3.0-flash-REAP384-97B-A5B with Docker Model Runner:
```
docker model run hf.co/bloomer010/Ling-3.0-flash-REAP384-97B-A5B
```

This is an experimental REAP.

Ling-3.0-flash REAP384 (97B total / 5.1B active)

[384 of 512 routed experts kept per layer - 25% of experts pruned] from inclusionAI/Ling-3.0-flash (124B total / 5.1B active).

Method: one-shot REAP (Router-weighted Expert Activation Pruning) - experts scored by router-gate-value × output-L2-norm over calibration data, lowest-scoring deleted. No fine-tuning, no recovery training.

Calibration: 1M tokens of ultrachat (chat-only calibration)

BF16 safetensors. Loads with trust_remote_code=True (custom bailing_hybrid / BailingMoeV3 code). Research artifact - quantized builds live in the sibling -GGUF repo.

Downloads last month: 680

Safetensors

Model size

97B params

Tensor type

F32

BF16

Model tree for bloomer010/Ling-3.0-flash-REAP384-97B-A5B

Base model

inclusionAI/Ling-3.0-flash

Finetuned

(6)

this model

Paper for bloomer010/Ling-3.0-flash-REAP384-97B-A5B

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 20