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	---
	base_model:
	- nvidia/Cosmos-Reason2-8B
	tags:
	- nvidia
	- cosmos
	- cosmos-reason2
	- multimodal
	- vlm
	- quantized
	- flashhead
	- qwen3_vl
	pipeline_tag: image-text-to-text
	license: other
	license_name: embedl-models-community-licence-1.0
	license_link: https://github.com/embedl/embedl-models/blob/main/LICENSE
	extra_gated_prompt: >-
	The information you provide will be collected, stored, processed and shared in accordance
	with the [Embedl Privacy Policy](https://www.embedl.com/privacy-policy).
	extra_gated_fields:
	Company: text
	---
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	# Cosmos-Reason2-8B-W4A16-FlashHead

	[![GitHub](https://img.shields.io/badge/GitHub-flash--head-black?logo=github)](https://github.com/embedl/flash-head)

	Optimized version of [nvidia/Cosmos-Reason2-8B](https://huggingface.co/nvidia/Cosmos-Reason2-8B) using quantization and FlashHead, Embedl's efficient replacement for the language model head.

	Designed for low-latency inference on NVIDIA GPUs, leveraging:

	- FlashHead
	- Quantization (W4A16)
	- vLLM plugin via [`flash-head`](https://github.com/embedl/flash-head)

	---

	## Model Details

	\| Field \| Value \|
	\|---\|---\|
	\| Base Model \| [nvidia/Cosmos-Reason2-8B](https://huggingface.co/nvidia/Cosmos-Reason2-8B) \|
	\| Input / Output \| Text + Image / Video -> Text \|
	\| Optimizations \| FlashHead LM Head + Quantization (W4A16) \|
	\| Developers \| Embedl \|
	\| Licenses \| Upstream: [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). <br>Optimized components: Embedl Models Community Licence v1.0 (no redistribution) \|

	---

	## Benchmarks

	Accuracy and on-device latency benchmarks can be explored on [embedl/Edge-Inference-Benchmarks](https://huggingface.co/spaces/embedl/Edge-Inference-Benchmarks).

	<a href="https://huggingface.co/spaces/embedl/Edge-Inference-Benchmarks" target="_blank" rel="noopener">
	<img src="https://huggingface.co/datasets/embedl/documentation-images/resolve/main/Edge-Inference-Benchmarks/screenshot.png" alt="Screenshot Edge Inference Benchmarks" width="75%">
	</a>

	---

	## Installation

	```bash
	pip install flash-head
	```

	The [`flash-head`](https://github.com/embedl/flash-head) vLLM plugin is required. It activates automatically at startup.

	---

	## Usage Examples

	### vLLM Serve

	```bash
	vllm serve embedl/Cosmos-Reason2-8B-W4A16-FlashHead \
	--max-model-len 8192 \
	--gpu-memory-utilization 0.75
	```

	### vLLM Video Inference

	```python
	from vllm import LLM, SamplingParams

	if __name__ == "__main__":
	model = "embedl/Cosmos-Reason2-8B-W4A16-FlashHead"
	video_url = "https://nvidia-cosmos.github.io/cosmos-cookbook/gallery/vs_assets/clip_1_short.mp4"

	messages = [
	{
	"role": "system",
	"content": [{"type": "text", "text": "You are a helpful assistant."}],
	},
	{
	"role": "user",
	"content": [
	{"type": "video_url", "video_url": {"url": video_url, "fps": 4}},
	{"type": "text", "text": "Describe this video in detail."},
	],
	},
	]

	llm = LLM(
	model=model,
	limit_mm_per_prompt={
	"video": {"count": 1, "num_frames": 12, "width": 1280, "height": 720},
	"image": 0,
	"audio": 0,
	},
	media_io_kwargs={"video": {"num_frames": -1}},
	max_model_len=8192,
	mm_processor_kwargs={"truncation": False},
	gpu_memory_utilization=0.75,
	trust_remote_code=True,
	)

	output = llm.chat(messages, sampling_params=SamplingParams(temperature=0.0, max_tokens=256))
	print(output[0].outputs[0].text)
	```

	---

	## License

	- Upstream: [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)
	- Optimized Components: Embedl Models Community Licence v1.0 (no redistribution)

	---

	## Contact

	- Enterprise and Commercial Inquiries: `models@embedl.com`
	- Technical Issues and Early Access: [`https://github.com/embedl/flash-head`](https://github.com/embedl/flash-head)
	- More Information and Model Releases: `https://embedl.com`

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