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Qwen3.6 27b Prismaquant 5.5bit Vllm model by rdtand
⭐ 56.4
🧠
Model

Qwen3.6 27b Prismaquant 5.5bit Vllm

by rdtand rdtand/qwen3.6-27b-prismaquant-5.5bit-vllm
Free2AITools Nexus Index
56.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 49
P: Popularity 46
R: Recency 87
Q: Quality 65
Tech Context
18.09 Params
32.768K Ctx
Vital Performance
14.5K DL / 30D
Source β†’
Low FNI signal 56.4 FNI Score
18.09B Params
32k Context
14.5K Downloads
24G GPU ~17GB Est. VRAM
Dense QWEN3_5FORCONDITIONALGENERATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID rdtand/qwen3.6-27b-prismaquant-5.5bit-vllm
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~16.1GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{rdtand_qwen3_6_27b_prismaquant_5_5bit_vllm,
  author = {rdtand},
  title = {Qwen3.6 27b Prismaquant 5.5bit Vllm Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/rdtand/Qwen3.6-27B-PrismaQuant-5.5bit-vllm}},
  note = {Accessed via Free2AITools.}
}
APA Style
rdtand. (2026). Qwen3.6 27b Prismaquant 5.5bit Vllm [Model]. Free2AITools. https://huggingface.co/rdtand/Qwen3.6-27B-PrismaQuant-5.5bit-vllm

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run qwen3.6-27b-prismaquant-5.5bit-vllm
πŸ€— HF Download
huggingface-cli download rdtand/qwen3.6-27b-prismaquant-5.5bit-vllm
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) 49
Popularity (P) 46
Recency (R) 87
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Qwen3.6 27b Prismaquant 5.5bit Vllm: Authority (A:49), Popularity (P:46), Recency (R:87), 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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πŸš€ What's Next?

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Deployment reference

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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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
14.5KDownloads
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--rdtand--qwen3.6-27b-prismaquant-5.5bit-vllm
slug
rdtand--qwen3.6-27b-prismaquant-5.5bit-vllm
source
huggingface
author
rdtand
license
Apache-2.0
tags
transformers, safetensors, qwen3_5, image-text-to-text, prismaquant, compressed-tensors, nvfp4, mxfp8, quantized, multimodal, vision-language, mtp, speculative-decoding, vllm, qwen3.6, conversational, en, zh, base_model:qwen/qwen3.6-27b, base_model:quantized:qwen/qwen3.6-27b, license:apache-2.0, endpoints_compatible, 8-bit, region:us

βš™οΈ Technical Specs

architecture
Qwen3_5ForConditionalGeneration
params billions
18.09
context length
32,768
pipeline tag
image-text-to-text
vram gb
16.1
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
14,537
stars
null
forks
null

Data indexed from public sources. Updated daily.