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Facu Vlad J

groxaxo

Cloud Engineer · AI Automation Engineer · Quantization Gremlin I build cloud systems, automate the boring parts, and squeeze absurd efficiency out of AI models. Into infra, agents, vLLM, local GPU rigs, and quantizations that make big models run where they probably shouldn’t.

Models in Library4
Datasets in Library0
Models on Hugging Face93
Followers40

Compare the published evidence

This profile connects the 4 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: Text generation, Text to speech.

Reported licenses: apache-2.0, other. 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 · Text generation

Hemmingway-1-AutoRound-W4G128

Facu Vlad J

4-bit, group-size-128 AutoRound quantization of Altworld/Hemmingway-1, a 27B-parameter fine-tune of Qwen3.8-27B. This repository is an independent quantization, not the original model release. See the original model card for training, intended use, and benchmark information. Quantization: AutoRound 0.15.1, bits=4, groupsize=128, packingformat=autoround:autoawq, seqlen=1024, nsamples=64, iters=50. Certain layers remain in higher precision, as specified in quantizationconfig.json. The included six-shard weight index and auxiliary modelextratensors.safetensors are needed together. Approximate download size: 18.65 GB (17.37 GiB). This is a quantization of an existing fine-tuned model, not a new…

Open weights apache-2.0 5.8B parameters 262,144 tokens transformers
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I wanted a smaller GGUF I could run, and I wanted to know more than just whether it loaded. This is where that experiment landed. How we got here has the story, including the first Q2 export, the H64-S4 experiment, the interrupted run, and what I could and couldn't verify. You need a runtime that supports the matching Hadamard transform. A runtime that ignores the rotation metadata can compute the wrong result. The tested llama.cpp changes, source patch, and build instructions are in runtime/. The export has nine GGUF shards, about 68.9 GB in total. It is based on Qwen/Qwen3.8-Flash-Next, revision de4b8e4d43b917e7706784d8bb445c9af86a3540. I used mixed precision to spend the bits where the…

Open weights other gguf
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Model · Text to speech

MOSS-TTS-8B-Q6-HQ8Speech-G64

Facu Vlad J

This is an MLX model for Apple Silicon. It is not directly compatible with the PyTorch CUDA runner. Converted from mlx-community/MOSS-TTS-8B-8bit at revision b9262754ad5245942472c7b4ff2b53cec3a07ec0: Q6 backbone and Q8 speech path, affine quantization with group size 64. Structurally validated on Linux; Apple Silicon runtime and speech-quality validation are pending. See RESULT.md, recipe.json, and the audit reports for details. The upstream model card follows. This MLX implementation brings MOSS-TTS to Apple Silicon through the MLX runtime, adapting the TTS backbone, multi-head audio token generation, and MOSS Audio Tokenizer workflow to run inside the MLX-Audio stack. As part of…

Open weights apache-2.0 40,960 tokens
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