Instructions to use Jackrong/Qwopus3.6-27B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jackrong/Qwopus3.6-27B-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Jackrong/Qwopus3.6-27B-v2")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Jackrong/Qwopus3.6-27B-v2")
model = AutoModelForImageTextToText.from_pretrained("Jackrong/Qwopus3.6-27B-v2")
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

vLLM

How to use Jackrong/Qwopus3.6-27B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jackrong/Qwopus3.6-27B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jackrong/Qwopus3.6-27B-v2",
		"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/Jackrong/Qwopus3.6-27B-v2

SGLang

How to use Jackrong/Qwopus3.6-27B-v2 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 "Jackrong/Qwopus3.6-27B-v2" \
    --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": "Jackrong/Qwopus3.6-27B-v2",
		"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 "Jackrong/Qwopus3.6-27B-v2" \
        --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": "Jackrong/Qwopus3.6-27B-v2",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio new

How to use Jackrong/Qwopus3.6-27B-v2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/Qwopus3.6-27B-v2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jackrong/Qwopus3.6-27B-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jackrong/Qwopus3.6-27B-v2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Jackrong/Qwopus3.6-27B-v2",
    max_seq_length=2048,
)

Docker Model Runner
How to use Jackrong/Qwopus3.6-27B-v2 with Docker Model Runner:
```
docker model run hf.co/Jackrong/Qwopus3.6-27B-v2
```

Significantly Lower Context Tokens (nctx) Compared to preview-v1 Variant?

by neoresin - opened about 22 hours ago

Discussion

neoresin

about 22 hours ago

I have been eagerly awaiting this model for so long! Thank you and Kyle Hessling so very much for performing this work and creating this model! 🙇 It's truly awesome.

I've been using the preview-v1 model for a fair bit with Opencode and I was able to hit 166400 tokens before compaction pretty reliably which gave a good size context window to work with. With this model, though, same Q4_K_M quantization, I only hit ~104000 tokens before it OOMs on my 4090.

Here's my commandline for both models:
/usr/bin/llama-server --port 7996 --ctx-size 166400 --fit on --cache-type-k q8_0 --cache-type-v q8_0 -fa on --api-key 'XXX' --repeat-penalty 1.0 --temp 0.6 --top-p 0.95 --min-p 0.0 --top-k 20 --presence-penalty 0.0 --image-min-tokens 1024 --chat-template-kwargs '{"preserve_thinking":true}' --reasoning on --jinja --chat-template-file '/dir/Qwen-Fixed-Chat-Templates/chat_template.jinja' --mmproj '/models/Qwopus3.6-27B-v1-preview-mmproj.gguf' --model '/models/Qwopus3.6-27B-v1-preview-Q4_K_M.gguf'
/usr/bin/llama-server --port 8076 --ctx-size 166400 --fit on --cache-type-k q8_0 --cache-type-v q8_0 -fa on --api-key 'XXX' --repeat-penalty 1.0 --temp 0.6 --top-p 0.95 --min-p 0.0 --top-k 20 --presence-penalty 0.0 --image-min-tokens 1024 --chat-template-kwargs '{"preserve_thinking":true}' --reasoning on --jinja --chat-template-file '/dir/Qwen-Fixed-Chat-Templates/chat_template.jinja' --mmproj '/models/Qwopus3.6-27B-v2-mmproj.gguf' --model '/models/Qwopus3.6-27B-v2-Q4_K_M.gguf'

I'm not claiming to know everything when it comes to all this, but it just seems strange that it would drop by so much if it's nearly the same model as the preview-v1. Is there an explanation for this? Thanks in advance for any time spent! Sorry if I missed anything in your Model card that should explain this.

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