Instructions to use CortexLM/Cortex-Mini-1-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CortexLM/Cortex-Mini-1-Preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="CortexLM/Cortex-Mini-1-Preview")
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("CortexLM/Cortex-Mini-1-Preview")
model = AutoModelForMultimodalLM.from_pretrained("CortexLM/Cortex-Mini-1-Preview", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CortexLM/Cortex-Mini-1-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CortexLM/Cortex-Mini-1-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CortexLM/Cortex-Mini-1-Preview",
		"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/CortexLM/Cortex-Mini-1-Preview

SGLang

How to use CortexLM/Cortex-Mini-1-Preview 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 "CortexLM/Cortex-Mini-1-Preview" \
    --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": "CortexLM/Cortex-Mini-1-Preview",
		"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 "CortexLM/Cortex-Mini-1-Preview" \
        --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": "CortexLM/Cortex-Mini-1-Preview",
		"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 CortexLM/Cortex-Mini-1-Preview with Docker Model Runner:
```
docker model run hf.co/CortexLM/Cortex-Mini-1-Preview
```

Cortex-Mini-1-Preview / README.md

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	---
	license: apache-2.0
	license_name: apache-2.0
	license_link: https://www.apache.org/licenses/LICENSE-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	language:
	- en
	- zh
	base_model: Qwen/Qwen3.8-27B
	base_model_relation: finetune
	tags:
	- qwen
	- qwen3.8
	- cortex
	- cortexlm
	- post-training
	- vision-language
	- conversational
	- preview
	---

	<p align="center">
	<img src="banner.jpg" alt="Cortex" width="100%" />
	</p>

	# Cortex-Mini-1-Preview

	Cortex-Mini-1-Preview is a post-trained derivative of [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B), released by [CortexLM](https://huggingface.co/CortexLM).

	This repository is a fork of the official Qwen3.8-27B checkpoint. We start from that open-weight native vision-language model and continue with Cortex post-training (Relearn). The goal is to improve the model on held-out tasks without collapsing general capability or overfitting public eval splits.

	> Preview. Weights, training recipe, and numbers in this card will move. Treat this page as the public home for the Cortex Mini 1 line, not a frozen release.

	## Thanks

	This work exists because the Qwen team open-sourced [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) under Apache 2.0.

	Thank you to the Qwen Team and Alibaba Cloud for the architecture, the training, the weights, and the docs. Cortex-Mini-1-Preview inherits their model; our contribution is post-training on top. If you use this checkpoint, please also cite Qwen3.8 (see [Citation](#citation)).

	## What this is

	\| \| \|
	\|---\|---\|
	\| Base \| [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) \|
	\| Relation \| Post-training / continued training (`finetune`) \|
	\| Type \| Native vision-language causal LM (text, image, video) \|
	\| Parameters \| 27B dense \|
	\| License \| Apache License 2.0 (same as the base) \|
	\| Org \| [CortexLM](https://huggingface.co/CortexLM) \|

	Qwen3.8-27B already accepts image and video input. Cortex-Mini-1-Preview does not bolt on a separate vision encoder. Miners and researchers post-train this 27B checkpoint itself.

	## License

	The base model is licensed under Apache License 2.0, Copyright 2026 Alibaba Cloud. That license allows commercial use, modification, and redistribution, including this fork.

	This repository redistributes Qwen3.8-27B weights and configuration as a derivative work:

	- The Apache 2.0 license text ships with the repo (`LICENSE`).
	- Attribution to the Qwen Team / Alibaba Cloud is retained (this card + `NOTICE`).
	- Files we change are marked as such. The model card, banner, and Cortex post-training artifacts are new.

	You must keep the license, attribution, and NOTICE if you redistribute further. "Qwen" remains a trademark of the original authors; this project does not claim that mark.

	Full terms: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) and the `LICENSE` file in this repo.

	## Model overview (inherited from Qwen3.8-27B)

	These architectural facts come from the [Qwen3.8-27B model card](https://huggingface.co/Qwen/Qwen3.8-27B). Cortex post-training does not change the network shape.

	- Type: Causal language model with vision encoder (native VLM)
	- Training stage (base): Pre-training and post-training by Qwen
	- Training stage (this repo): Additional Cortex post-training on the Qwen checkpoint
	- Parameters: 27B
	- Hidden size: 5120
	- Layers: 64
	- Context: 262,144 tokens natively, extensible to 1,000,000 with YaRN
	- Modalities: Text, image, video

	For serving details, sampling defaults, thinking mode, and YaRN flags, follow the Qwen card. Those recipes still apply.

	## Intended use

	- Research and development on Cortex Relearn (post-train a capable open VLM without wrecking general skills).
	- Downstream chat, coding, agent, and vision-language workloads that already run on Qwen3.8-27B.
	- Further fine-tuning under Apache 2.0.

	Out of scope

	- Do not present this preview as a drop-in Qwen Cloud / Qwen3.8-Max replacement.
	- Do not treat unpublished scores as Qwen's official numbers.
	- Do not strip license or attribution.

	## How to use

	Load the repo id once weights are in place:

	```python
	from transformers import AutoModelForImageTextToText, AutoProcessor

	model_id = "CortexLM/Cortex-Mini-1-Preview"
	processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForImageTextToText.from_pretrained(
	model_id,
	torch_dtype="auto",
	device_map="auto",
	trust_remote_code=True,
	)
	```

	For production serving, use vLLM, SGLang, or TokenSpeed with the local directory of the downloaded weights, not a guessed path. See Qwen's [vLLM recipe](https://recipes.vllm.ai/Qwen/Qwen3.8-27B) and [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/Qwen/Qwen3.8-27B).

	Thinking mode is on by default in the Qwen3.8 stack (`enable_thinking`, `preserve_thinking`, `reasoning_effort`). Keep those defaults unless you know you want instruct/non-thinking sampling.

	## Evaluation

	Preview: we do not publish a public leaderboard on this card yet. Cortex scores live training on a private holdout. Public splits are informational. General-capability checks run off the visible reward so they are not an overfitting target.

	When a numbered release ships, this section will get frozen metrics and the eval recipe.

	## Limitations

	- Preview checkpoint: APIs, tokenizer extras, and chat template can still change.
	- Post-training can move behavior away from the Qwen defaults (style, refusal, tool use). Compare against [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) before you swap it in.
	- Long-context and hour-scale video settings are inherited from Qwen; we have not re-tuned those knobs here.
	- As with any open VLM, outputs can be wrong, biased, or unsafe. You are responsible for downstream filters.

	## Files of ours vs Qwen's

	\| File \| Source \|
	\|---\|---\|
	\| Weights, tokenizer, `config.json`, processor configs \| Forked from Qwen/Qwen3.8-27B \|
	\| `LICENSE` \| Apache 2.0 from the base (Copyright 2026 Alibaba Cloud) \|
	\| `README.md`, `banner.jpg`, `NOTICE` \| CortexLM \|

	## Citation

	If you use Cortex-Mini-1-Preview, please cite both this work and Qwen3.8:

	```bibtex
	@misc{cortex-mini-1-preview,
	title = {Cortex-Mini-1-Preview},
	author = {CortexLM},
	year = {2026},
	url = {https://huggingface.co/CortexLM/Cortex-Mini-1-Preview}
	}

	@misc{qwen38,
	title = {{Qwen3.8-Max}: A New Bar for Coding and Cowork},
	url = {https://qwen.ai/blog?id=qwen3.8},
	author = {{Qwen Team}},
	month = {August},
	year = {2026}
	}
	```

	## Links

	- Base model: [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)
	- Qwen collection: [Qwen3.8 on Hugging Face](https://huggingface.co/collections/Qwen/qwen38)
	- CortexLM: [huggingface.co/CortexLM](https://huggingface.co/CortexLM)
	- Cortex: [github.com/CortexLM/cortex](https://github.com/CortexLM/cortex)