Instructions to use inclusionAI/Ling-3.0-flash-dspark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-3.0-flash-dspark with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ling-3.0-flash-dspark")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ling-3.0-flash-dspark")
model = AutoModel.from_pretrained("inclusionAI/Ling-3.0-flash-dspark", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ling-3.0-flash-dspark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-3.0-flash-dspark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-3.0-flash-dspark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-3.0-flash-dspark

SGLang

How to use inclusionAI/Ling-3.0-flash-dspark 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 "inclusionAI/Ling-3.0-flash-dspark" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-3.0-flash-dspark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "inclusionAI/Ling-3.0-flash-dspark" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-3.0-flash-dspark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inclusionAI/Ling-3.0-flash-dspark with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-3.0-flash-dspark
```

Link the SGLang Ling-3.0-flash cookbook from the serving section

by JustinTong - opened 4 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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README.md +2 -0

README.md CHANGED Viewed

@@ -47,6 +47,8 @@ The macro mean across the nine workload means is **5.29**.
 ## Serving with SGLang
 Use an SGLang version with DSPARK support. Replace the model paths and tensor-parallel size with values appropriate for your deployment:
 ```bash

 ## Serving with SGLang
+Launch recipes for this draft on every supported hardware/quantization cell — including the required `--linear-replayssm-cache-len` sizing — with measured speed and accuracy, are in the [SGLang Ling-3.0-flash cookbook](https://docs.sglang.io/cookbook/autoregressive/InclusionAI/Ling-3.0-flash).
 Use an SGLang version with DSPARK support. Replace the model paths and tensor-parallel size with values appropriate for your deployment:
 ```bash