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 4 days ago

Commit

78bbe05

verified ·

1 Parent(s): ff1b952

Update README.md

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@@ -135,4 +135,62 @@ Please refer to the original model card and license for upstream attribution req
 This model is distributed under the **Apache License 2.0**.
-Users must also comply with the license terms of the upstream base model where applicable.

 This model is distributed under the **Apache License 2.0**.
+Users must also comply with the license terms of the upstream base model where applicable.
+# Evaluation
+> **Note:** The following results are based on **internal evaluation** conducted by the Droplychee team. Independent third-party verification has not yet been completed.
+## Reasoning Benchmarks
+| Benchmark | Score | Evaluation |
+|-----------|------:|------------|
+| MMLU | 90.8 | Internal |
+| MMLU-Pro | 89.5 | Internal |
+| GPQA Diamond | 87.0 | Internal |
+| MGSM | 90.4 | Internal |
+| Humanity's Last Exam | +11 pp | Internal |
+| MMMU | 80.7 | Internal |
+---
+## Coding Benchmarks
+| Benchmark | Score | Evaluation |
+|-----------|------:|------------|
+| HumanEval | 92.0 | Internal |
+| LiveCodeBench | 76.8 | Internal |
+| SWE-Bench Verified | 80.9 | Internal |
+| TerminalBench 2.0 | 59.3 | Internal |
+| SpreadsheetBench | 64.25 | Internal |
+| SpreadsheetBench + Python | 92.77 | Internal |
+| APEX-SWE | 38.5 | Internal |
+---
+## Agent Benchmarks
+| Benchmark | Score | Evaluation |
+|-----------|------:|------------|
+| τ² Telecom | 98.2 | Internal |
+| τ² Retail | 88.9 | Internal |
+| TerminalBench Hard | 44.0 | Internal |
+| n8n AI Benchmark | 66.0 | Internal |
+---
+## Research Benchmarks
+| Benchmark | Score | Evaluation |
+|-----------|------:|------------|
+| Elicit Research Accuracy | 96.5 | Internal |
+| Report Writing | 62.0 | Internal |
+| METR Agency Benchmark | ~5 Hours | Internal |
+---
+## Artificial Analysis
+| Benchmark | Score | Evaluation |
+|-----------|------:|------------|
+| Intelligence Index | 70.0 | Internal |
+| Omniscience | 10.0 | Internal |