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D3 W4a16 Gptq model by Vllm Sr
⭐ 47.7
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Model

D3 W4a16 Gptq

by Vllm Sr vllm-sr/d3-w4a16-gptq
Free2AITools Nexus Index
47.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 39
P: Popularity 11
R: Recency 99
Q: Quality 65
Tech Context
26.09 Params
32.768K Ctx
Vital Performance
155 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 47.7 FNI Score
26.09B Params
32k Context
155 Downloads
24G GPU ~23GB Est. VRAM
Dense QWEN3_5MODEL Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID vllm-sr/d3-w4a16-gptq
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~22.1GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{vllm_sr_d3_w4a16_gptq,
  author = {Vllm Sr},
  title = {D3 W4a16 Gptq Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/vllm-sr/d3-W4A16-GPTQ}},
  note = {Accessed via Free2AITools.}
}
APA Style
Vllm Sr. (2026). D3 W4a16 Gptq [Model]. Free2AITools. https://huggingface.co/vllm-sr/d3-W4A16-GPTQ

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run d3-w4a16-gptq
πŸ€— HF Download
huggingface-cli download vllm-sr/d3-w4a16-gptq
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 39
Popularity (P) 11
Recency (R) 99
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for D3 W4a16 Gptq: Authority (A:39), Popularity (P:11), Recency (R:99), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Technical Deep Dive

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

HuggingFace Hub
155Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--vllm-sr--d3-w4a16-gptq
slug
vllm-sr--d3-w4a16-gptq
source
huggingface
author
Vllm Sr
license
Apache-2.0
tags
transformers, safetensors, qwen3_5, feature-extraction, zero-shot-classification, decision-model, classification, system-one, multimodal, vision, video, quantized, compressed-tensors, gptq, w4a16, custom_code, base_model:vllm-sr/d3, base_model:quantized:vllm-sr/d3, license:apache-2.0, eval-results, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
Qwen3_5Model
params billions
26.09
context length
32,768
pipeline tag
zero-shot-classification
vram gb
22.1
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
155

Data indexed from public sources. Updated daily.