Instructions to use rodrigomt/Qwen-3.5-Opus-GLM-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rodrigomt/Qwen-3.5-Opus-GLM-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="rodrigomt/Qwen-3.5-Opus-GLM-27B")
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("rodrigomt/Qwen-3.5-Opus-GLM-27B")
model = AutoModelForImageTextToText.from_pretrained("rodrigomt/Qwen-3.5-Opus-GLM-27B")
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 rodrigomt/Qwen-3.5-Opus-GLM-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rodrigomt/Qwen-3.5-Opus-GLM-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rodrigomt/Qwen-3.5-Opus-GLM-27B",
		"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/rodrigomt/Qwen-3.5-Opus-GLM-27B

SGLang

How to use rodrigomt/Qwen-3.5-Opus-GLM-27B 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 "rodrigomt/Qwen-3.5-Opus-GLM-27B" \
    --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": "rodrigomt/Qwen-3.5-Opus-GLM-27B",
		"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 "rodrigomt/Qwen-3.5-Opus-GLM-27B" \
        --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": "rodrigomt/Qwen-3.5-Opus-GLM-27B",
		"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 rodrigomt/Qwen-3.5-Opus-GLM-27B with Docker Model Runner:
```
docker model run hf.co/rodrigomt/Qwen-3.5-Opus-GLM-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen-3.5-Opus-GLM-27B

A DARE-TIES merge combining the strengths of two fine-tuned Qwen 3.5 27B variants — one distilled from Opus-style reasoning, the other from GLM 5.1 — into a single unified model.

Source Models

Model	Role	Density	Weight
Qwopus3.5-27B-v3.5	Donor A	0.60	0.55
Qwen3.5-27B-GLM5.1-Distill-v1	Donor B	0.50	0.45
unsloth/Qwen3.5-27B	Base	—	—

Merge Configuration

models:
  - model: Jackrong/Qwopus3.5-27B-v3.5
    parameters:
      density: 0.6
      weight: 0.55
  - model: Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1
    parameters:
      density: 0.5
      weight: 0.45
merge_method: dare_ties
base_model: unsloth/Qwen3.5-27B
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

Quick Start (4-bit QLoRA-ready)

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model_id = "rodrigomt/Qwen-3.5-Opus-GLM-27B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
)

Downloads last month: 8

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for rodrigomt/Qwen-3.5-Opus-GLM-27B

Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1

Jackrong/Qwopus3.5-27B-v3.5

Merge model

this model

Quantizations

5 models