Instructions to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next with Transformers:

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

pipe = pipeline("image-text-to-text", model="bottlecapai/ThinkingCap-Qwen3.8-Flash-Next")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("bottlecapai/ThinkingCap-Qwen3.8-Flash-Next")
model = AutoModelForMultimodalLM.from_pretrained("bottlecapai/ThinkingCap-Qwen3.8-Flash-Next", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next",
		"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/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next

SGLang

How to use bottlecapai/ThinkingCap-Qwen3.8-Flash-Next 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 "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next" \
    --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": "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next",
		"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 "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next" \
        --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": "bottlecapai/ThinkingCap-Qwen3.8-Flash-Next",
		"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 bottlecapai/ThinkingCap-Qwen3.8-Flash-Next with Docker Model Runner:
```
docker model run hf.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next
```

Adamiros commited on 5 days ago

Commit

a75e6f6

verified ·

1 Parent(s): d90c9fe

Card: point to the RCO 3.8bpw GGUF

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@@ -153,6 +153,7 @@ Same checkpoint, chat template and license, quantized for smaller footprints:
 - **FP8** &mdash; [bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-FP8](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-FP8) &mdash; vLLM; Qwen's own FP8 layout (FP8 routed experts and n-gram table), ≈186 GB
 - **NVFP4 (W4A16)** &mdash; [bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-NVFP4](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-NVFP4) &mdash; vLLM; 4-bit weight-only routed experts, ≈132 GB
 - **GGUF** &mdash; [bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF) &mdash; llama.cpp; BF16, Q8_0, Q6_K, Q4_K_M and IQ4_XS, with the vision projector
 ## Where to find us

 - **FP8** &mdash; [bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-FP8](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-FP8) &mdash; vLLM; Qwen's own FP8 layout (FP8 routed experts and n-gram table), ≈186 GB
 - **NVFP4 (W4A16)** &mdash; [bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-NVFP4](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-NVFP4) &mdash; vLLM; 4-bit weight-only routed experts, ≈132 GB
 - **GGUF** &mdash; [bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF) &mdash; llama.cpp; BF16, Q8_0, Q6_K, Q4_K_M and IQ4_XS, with the vision projector
+- **GGUF, RCO 3.8 bpw** &mdash; [bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF-RCO-3.8bpw](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-Flash-Next-GGUF-RCO-3.8bpw) &mdash; llama.cpp; per-tensor mixed precision from IST-DASLab's RCO allocation, 85.6 GB, of which 56.5 GB on the GPU, with the vision projector
 ## Where to find us