Instructions to use Bottlepick/dend-dpo100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bottlepick/dend-dpo100 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Bottlepick/dend-dpo100")
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("Bottlepick/dend-dpo100")
model = AutoModelForMultimodalLM.from_pretrained("Bottlepick/dend-dpo100", 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 Bottlepick/dend-dpo100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bottlepick/dend-dpo100"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bottlepick/dend-dpo100",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Bottlepick/dend-dpo100

SGLang

How to use Bottlepick/dend-dpo100 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 "Bottlepick/dend-dpo100" \
    --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": "Bottlepick/dend-dpo100",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Bottlepick/dend-dpo100" \
        --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": "Bottlepick/dend-dpo100",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Bottlepick/dend-dpo100 with Docker Model Runner:
```
docker model run hf.co/Bottlepick/dend-dpo100
```

albedo-qwen3.6-35b-dpo100

DPO checkpoint-100, merged to full weights.

Provenance


base	king v96 (`dendriteholdings/albedo-qwen3.6-35b-king-XCVI` @ `2a6708b8`, 22 shards)
adapter	`onpolicy_v96_run1` DPO LoRA, checkpoint-100 (r32, alpha32)
merge	350 modules resolved; `config.json` matches `GENESIS_MODEL_CONFIG` with 0 field differences
arch	`qwen3_5_moe`, 22 safetensors shards

The adapter was trained against king v96, so v96 is the only correct merge base — merging it onto a different base produces garbage weights rather than a weaker model.

Evaluation

Scored locally under the SN97 regime that went live on mainnet 2026-07-30 ~13:52 (new evaluator prompt, z-ai/glm-5.2 as sole SOTA anchor and sole judge), on the 100 tasks published with eval run a3fd0092. See train_methods/gate_runs/glmonly_20260730/ in the sn97 repo.

Downloads last month: 27

Safetensors

Model size

35B params

Tensor type

BF16