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+Cosmos-Reason2-8B.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

Cosmos-Reason2-8B.Q8_0.gguf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0cd7dc26cb43a9e0a549372647c50923e72c2c95c8c00318d69d1c9765e9e868
+size 8709520128

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- nvidia/Cosmos-Reason2-8B
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- text-generation-inference
+- llama.cpp
+---
+
+# **Cosmos-Reason2-8B-GGUF**
+
+> NVIDIA Cosmos-Reason2-8B is an 8.767B-parameter open vision-language model post-trained from Qwen3-VL-8B-Instruct under the permissive NVIDIA Open Model License (commercially usable with derivative rights), designed for physical AI, robotics, and embodied reasoning with enhanced spatio-temporal understanding, physics-based common sense, object detection (2D/3D localization, bounding boxes), and 256K token long-context support for video/image inputs at FPS=4. It excels in use cases like video analytics agents (root-cause analysis via NVIDIA VSS Blueprint), data curation/annotation (Cosmos Curator for sensor data), and robot planning (Isaac GR00T-Dreams for synthetic trajectories), generating chain-of-thought responses in <think>/<answer> format for deliberate decision-making in unfamiliar environments across datasets like EgoExo4D, PerceptionTest, IntPhys, and CLEVRER. Optimized for NVIDIA Blackwell/Hopper GPUs (32GB+ VRAM, BF16 on Linux/H100/A100) with Transformers inference (max_tokens=4096), it handles mp4 videos/jpg images for text outputs in robotics, AVs, and industrial ops while noting limitations in complex dynamics like fast motion or occlusions.
+
+## Cosmos-Reason2-8B [GGUF]
+
+| File Name | Quant Type | File Size | File Link |
+| - | - | - | - |
+| Cosmos-Reason2-8B.BF16.gguf | BF16 | 16.4 GB | [Download](https://huggingface.co/prithivMLmods/Cosmos-Reason2-8B-GGUF/blob/main/Cosmos-Reason2-8B.BF16.gguf) |
+| Cosmos-Reason2-8B.F16.gguf | F16 | 16.4 GB | [Download](https://huggingface.co/prithivMLmods/Cosmos-Reason2-8B-GGUF/blob/main/Cosmos-Reason2-8B.F16.gguf) |
+| Cosmos-Reason2-8B.Q8_0.gguf | Q8_0 | 8.71 GB | [Download](https://huggingface.co/prithivMLmods/Cosmos-Reason2-8B-GGUF/blob/main/Cosmos-Reason2-8B.Q8_0.gguf) |
+| Cosmos-Reason2-8B.mmproj-bf16.gguf | mmproj-bf16 | 1.16 GB | [Download](https://huggingface.co/prithivMLmods/Cosmos-Reason2-8B-GGUF/blob/main/Cosmos-Reason2-8B.mmproj-bf16.gguf) |
+| Cosmos-Reason2-8B.mmproj-f16.gguf | mmproj-f16 | 1.16 GB | [Download](https://huggingface.co/prithivMLmods/Cosmos-Reason2-8B-GGUF/blob/main/Cosmos-Reason2-8B.mmproj-f16.gguf) |
+| Cosmos-Reason2-8B.mmproj-q8_0.gguf | mmproj-q8_0 | 752 MB | [Download](https://huggingface.co/prithivMLmods/Cosmos-Reason2-8B-GGUF/blob/main/Cosmos-Reason2-8B.mmproj-q8_0.gguf) |
+
+## Quants Usage 
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

config.json

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+{
+    "model_type": "qwen3_vl"
+}