Instructions to use droplychee/droplychee-2.0-40b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use droplychee/droplychee-2.0-40b with Transformers:

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

pipe = pipeline("text-generation", model="droplychee/droplychee-2.0-40b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("droplychee/droplychee-2.0-40b")
model = AutoModelForMultimodalLM.from_pretrained("droplychee/droplychee-2.0-40b", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use droplychee/droplychee-2.0-40b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "droplychee/droplychee-2.0-40b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "droplychee/droplychee-2.0-40b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/droplychee/droplychee-2.0-40b

SGLang

How to use droplychee/droplychee-2.0-40b 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 "droplychee/droplychee-2.0-40b" \
    --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": "droplychee/droplychee-2.0-40b",
		"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 "droplychee/droplychee-2.0-40b" \
        --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": "droplychee/droplychee-2.0-40b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use droplychee/droplychee-2.0-40b with Docker Model Runner:
```
docker model run hf.co/droplychee/droplychee-2.0-40b
```

MD-Mushfiqur123 commited on Jul 27

Commit

1819b71

verified ·

1 Parent(s): c3416d6

Update README.md

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@@ -196,5 +196,51 @@ Users must also comply with the license terms of the upstream base model where a
 | Omniscience | 10.0 | Internal |
 > **Disclaimer:** These benchmark results are derived from the Droplychee team's internal evaluation pipeline. Results may vary depending on hardware, inference engine, prompt format, evaluation methodology, and software versions. Independent third-party verification is planned for future releases.
->
->

 | Omniscience | 10.0 | Internal |
 > **Disclaimer:** These benchmark results are derived from the Droplychee team's internal evaluation pipeline. Results may vary depending on hardware, inference engine, prompt format, evaluation methodology, and software versions. Independent third-party verification is planned for future releases.
+For the latest documentation, technical reports, and official releases, please visit the official Droplychee GitHub repository.
+https://github.com/DropLychee/droplychee-2.0-40b
+droplychee-2.0-40b/
+│
+├── README.md
+├── LICENSE
+├── MODEL_CARD.md
+├── TECHNICAL_REPORT.md
+├── SYSTEM_CARD.md
+├── EVALUATION.md
+├── CITATION.cff
+├── CHANGELOG.md
+├── CONTRIBUTING.md
+├── SECURITY.md
+├── CODE_OF_CONDUCT.md
+├── docs/
+│   ├── architecture.md
+│   ├── training.md
+│   ├── datasets.md
+│   ├── tokenizer.md
+│   ├── inference.md
+│   ├── benchmarks.md
+│   ├── deployment.md
+│   ├── safety.md
+│   ├── governance.md
+│   └── roadmap.md
+├── assets/
+│   ├── logo.png
+│   ├── banner.png
+│   ├── architecture.svg
+│   └── benchmark_charts/
+├── examples/
+│   ├── transformers.py
+│   ├── vllm.py
+│   ├── llama_cpp.py
+│   └── openai_api.py
+├── scripts/
+│   ├── evaluate.py
+│   ├── benchmark.py
+│   ├── convert_gguf.py
+│   └── export.py
+└── paper/
+    ├── Droplychee-2.0-40B_Technical_Report.pdf
+    ├── Droplychee-2.0-40B_Technical_Report.md
+    └── references.bib