Instructions to use groxaxo/Qwevolution-V0-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use groxaxo/Qwevolution-V0-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="groxaxo/Qwevolution-V0-27B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("groxaxo/Qwevolution-V0-27B")
model = AutoModelForMultimodalLM.from_pretrained("groxaxo/Qwevolution-V0-27B")
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 Settings

vLLM

How to use groxaxo/Qwevolution-V0-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "groxaxo/Qwevolution-V0-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "groxaxo/Qwevolution-V0-27B",
		"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/groxaxo/Qwevolution-V0-27B

SGLang

How to use groxaxo/Qwevolution-V0-27B 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 "groxaxo/Qwevolution-V0-27B" \
    --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": "groxaxo/Qwevolution-V0-27B",
		"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 "groxaxo/Qwevolution-V0-27B" \
        --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": "groxaxo/Qwevolution-V0-27B",
		"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 groxaxo/Qwevolution-V0-27B with Docker Model Runner:
```
docker model run hf.co/groxaxo/Qwevolution-V0-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwevolution V0 27B

A weight-space blend of the ConicCat/Qwen3.5-27B-Writer-V2 vision-language model with the groxaxo/Qwen3.5-27B-Writer-V2-Heretic-Coding-LoRA coding LoRA adapter, merged with peft.merge_and_unload(safe_merge=True) in bfloat16.

Composition

Base model: ConicCat/Qwen3.5-27B-Writer-V2 — Qwen3_5ForConditionalGeneration, 27B, BF16, license apache-2.0. Base commit 55f457bed78b68442d25815bd156b98f2e6fd341.
LoRA adapter: groxaxo/Qwen3.5-27B-Writer-V2-Heretic-Coding-LoRA — r=16, α=32. Adapter commit 0b3508764f80741f612c251c7f47b1e210ac2e63.

Deliberate cross-base merge. The coding LoRA was originally trained on llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic. It was intentionally merged onto the clean ConicCat/Qwen3.5-27B-Writer-V2 (same Writer-V2 lineage / identical architecture, vocab and module tree).

What was merged (and what wasn't)

The adapter touched 607 modules. Of these, 496 language-tower modules carried trained (non-zero) weights and were merged into the base. The remaining 111 vision-tower modules were zero in the adapter itself (never trained) — merging them is a no-op, so the vision tower is identical to the base model and vision behaviour should match ConicCat/Qwen3.5-27B-Writer-V2.

Provenance & verification

Merged on CPU in bfloat16 with safe_merge=True (per-layer NaN check).
Full adapter coverage verified before merge; trained language weights confirmed loaded (non-zero B).
Integrity verified by reloading the saved checkpoint and running a forward pass (finite logits).
See merge_manifest.json for exact base/adapter commits and library versions.

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained("groxaxo/Qwevolution-V0-27B", dtype="bfloat16", device_map="auto")
processor = AutoProcessor.from_pretrained("groxaxo/Qwevolution-V0-27B")

Downloads last month: -

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for groxaxo/Qwevolution-V0-27B

ConicCat/Qwen3.5-27B-Writer-V2

groxaxo/Qwen3.5-27B-Writer-V2-Heretic-Coding-LoRA

Merge model

this model

Quantizations

1 model