Independent publisher

Vishva R

Vishva007

Deeply interested in Generative AI, particularly Large Language Models (LLMs). Focusing on model fine-tuning, prompt engineering, and practical applications using the Hugging Face ecosystem (Transformers, Datasets).

Models in Library2

Datasets in Library0

Models on Hugging Face70

Followers14

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This profile connects the 2 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Image and text to text.

Reported licenses: apache-2.0. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

Model · Image and text to text

Qwen3.5-9B-W4A16-AutoRound-NVFP4

Vishva R

This is an NVFP4 (W4A16: 4-bit weight, 16-bit activation) quantized version of Qwen/Qwen3.5-9B, produced using AutoRound — Intel's sign gradient descent based quantization method designed for production-grade accuracy retention. Exported via compressed-tensors (llmcompressor format) with MTP layers preserved. - NVFP4 Quantization — Weights are quantized to FP4 micro-scaled formats using group size 16; activations remain in 16-bit for stable numerical fidelity. - Vision Tower (visual / quantnontextmodule): Kept in BF16 to preserve visual reasoning and OCR precision. - Special Modules (ignorelayers & layerconfig): linearattn, mtp, and mtp.fc are strictly retained in native bfloat16 to ensure…

Open weights apache-2.0 7.1B parameters 262,144 tokens

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Model · Image and text to text

Qwen3.5-4B-W4A16-AutoRound-NVFP4

Vishva R

This is an NVFP4 (W4A16: 4-bit weight, 16-bit activation) quantized version of Qwen/Qwen3.5-4B, produced using AutoRound — Intel's sign gradient descent based quantization method designed for production-grade accuracy retention. Exported via compressed-tensors (llmcompressor format) with MTP layers preserved. - NVFP4 Quantization — Weights are quantized to FP4 micro-scaled formats using group size 16; activations remain in 16-bit for stable numerical fidelity. - Vision Tower (visual / quantnontextmodule): Kept in BF16 to preserve visual reasoning and OCR precision. - Special Modules (ignorelayers & layerconfig): linearattn, mtp, and mtp.fc are strictly retained in native bfloat16 to ensure…

Open weights apache-2.0 3.4B parameters 262,144 tokens

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