🧠
Model

Qwen3.5 27b Writer V2 Mlx Mixed 9.4bit

by TheCluster hf-model--thecluster--qwen3.5-27b-writer-v2-mlx-mixed-9.4bit
Nexus Index
39.5 Top 4%
S: Semantic 50
A: Authority 0
P: Popularity 11
R: Recency 98
Q: Quality 65
Tech Context
27.36 Params
32.768K Ctx
Vital Performance
150 DL / 30D
0.0%
Audited 39.5 FNI Score
27.36B Params
32k Context
Hot 150 Downloads
24G GPU ~23GB Est. VRAM
Dense QWEN3_5FORCONDITIONALGENERATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--thecluster--qwen3.5-27b-writer-v2-mlx-mixed-9.4bit
License Apache-2.0
Provider huggingface
💾

Compute Threshold

~23GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX
@misc{hf_model__thecluster__qwen3.5_27b_writer_v2_mlx_mixed_9.4bit,
  author = {TheCluster},
  title = {Qwen3.5 27b Writer V2 Mlx Mixed 9.4bit Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/TheCluster/Qwen3.5-27B-Writer-V2-MLX-mixed-9.4bit}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
TheCluster. (2026). Qwen3.5 27b Writer V2 Mlx Mixed 9.4bit [Model]. Free2AITools. https://huggingface.co/TheCluster/Qwen3.5-27B-Writer-V2-MLX-mixed-9.4bit

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run qwen3.5-27b-writer-v2-mlx-mixed-9.4bit
🤗 HF Download
huggingface-cli download thecluster/qwen3.5-27b-writer-v2-mlx-mixed-9.4bit

âš–ī¸ Nexus Index V2.0

39.5
TOP 4% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 11
Recency (R) 98
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Qwen3.5 27b Writer V2 Mlx Mixed 9.4bit: Semantic (S:50), Authority (A:0), Popularity (P:11), Recency (R:98), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

Technical Deep Dive

Qwen3.5-27B Writer V2

Quality: quantized (mixed quants per tensor, group size: 32, 9.450 bpw)

Most layers use 8-bit affine quantization with a group size 32; some layers are saved in bf16.

A writing & roleplay finetune of Qwen3.5 27B. The primary emphasis is on writing quality as it strongly generalizes across both domains.

  • Chatml template with <think>\n\n</think> or {{char}}: prefill. Only non-thinking was trained, but thinking probably still works.
  • temperature = 0.7
  • top_p = 0.95
  • I do not recommend using high rep pen values like Qwen suggests for the base model. rep_pen = 1.05 or a moderate dry setting should suffice.

Source

This model was converted to MLX format from ConicCat/Qwen3.5-27B-Writer-V2 using mlx-vlm version 0.4.4.

âš ī¸ 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 →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
Top Tier

Social Proof

HuggingFace Hub
150Downloads
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseâ„šī¸ Verify with original source

đŸ›Ąī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--thecluster--qwen3.5-27b-writer-v2-mlx-mixed-9.4bit
slug
thecluster--qwen3.5-27b-writer-v2-mlx-mixed-9.4bit
source
huggingface
author
TheCluster
license
Apache-2.0
tags
mlx, safetensors, qwen3_5, 9bit, roleplay, conversational, mixed-precision, image-text-to-text, en, zh, ru, es, fr, it, ja, ko, af, de, ar, tr, is, pl, sw, sv, nl, he, id, uk, fa, pa, pt, ms, fi, el, base_model:coniccat/qwen3.5-27b-writer-v2, license:apache-2.0, 8-bit, region:us

âš™ī¸ Technical Specs

architecture
Qwen3_5ForConditionalGeneration
params billions
27.36
context length
32,768
pipeline tag
image-text-to-text
vram gb
23
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
150
stars
0
forks
0

Data indexed from public sources. Updated daily.