Instructions to use Lambent/Enteles-v0-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lambent/Enteles-v0-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Lambent/Enteles-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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Lambent/Enteles-v0-27B")
model = AutoModelForImageTextToText.from_pretrained("Lambent/Enteles-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

vLLM

How to use Lambent/Enteles-v0-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lambent/Enteles-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": "Lambent/Enteles-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/Lambent/Enteles-v0-27B

SGLang

How to use Lambent/Enteles-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 "Lambent/Enteles-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": "Lambent/Enteles-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 "Lambent/Enteles-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": "Lambent/Enteles-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 Lambent/Enteles-v0-27B with Docker Model Runner:
```
docker model run hf.co/Lambent/Enteles-v0-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

They're a bit confused, but they got the spirit :)

Mostly wanted a capable version of Qwen3.5-27B at hand who wasn't too strait-laced. They'd probably benefit from a dash more training to cohere their weights together a bit. Sometimes they toss medical reasoning traces into unrelated contexts. Sometimes they forget to output after ending their reasoning, or put the output in their reasoning.

Seem neat, tho.

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the WAVE merge method using Qwen/Qwen3.5-27B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# Enteles v2: WAVE merge, 3 models (dropped Gliese LLM weights)
# Vision weights grafted from Gliese post-merge via graft_vision.py
# heretic-v3 (Arbitrary-Rank Ablation) replaces v2 — 83.3 eq_bench vs 64.4
#
# Post-merge:
#   python graft_vision.py --donor <Gliese-local-path> --output ./merged-output --vision-prefix model.visual
#   python pad_embeddings.py --model ./merged-output --target-vocab 248320
models:
  - model: ValiantLabs/Qwen3.5-27B-Guardpoint
    parameters:
      weight: 0.35
  - model: ConicCat/Qwen3.5-27B-Writer
    parameters:
      weight: 0.35
  - model: llmfan46/Qwen3.5-27B-heretic-v3
    parameters:
      weight: 0.3
merge_method: wave
base_model: Qwen/Qwen3.5-27B
parameters:
  synergy: 0.6
  entropy: 0.05
dtype: bfloat16
tokenizer_source: Qwen/Qwen3.5-27B
pad_to_multiple_of: 256

Downloads last month: 1

Safetensors

Model size

28B params

Tensor type

BF16

Model tree for Lambent/Enteles-v0-27B

ConicCat/Qwen3.5-27B-Writer

Qwen/Qwen3.5-27B

ValiantLabs/Qwen3.5-27B-Guardpoint

llmfan46/Qwen3.5-27B-heretic-v3

Merge model

this model

Quantizations

2 models