🧠
Qwen3 Coder Next Oq2 model by bearzi
⭐ 40
🧠
Model

Qwen3 Coder Next Oq2

by bearzi hf-model--bearzi--qwen3-coder-next-oq2
Nexus Index
40.0 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 16
R: Recency 98
Q: Quality 65
Tech Context
Vital Performance
317 DL / 30D
0.0%
Source →
Audited 40 FNI Score
Tiny - Params
- Context
317 Downloads
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--bearzi--qwen3-coder-next-oq2
License Apache-2.0
Provider huggingface
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__bearzi__qwen3_coder_next_oq2,
  author = {bearzi},
  title = {Qwen3 Coder Next Oq2 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/bearzi/qwen3-coder-next-oq2}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
bearzi. (2026). Qwen3 Coder Next Oq2 [Model]. Free2AITools. https://huggingface.co/bearzi/qwen3-coder-next-oq2

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download bearzi/qwen3-coder-next-oq2

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
40.0
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 16
Recency (R) 98
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Qwen3 Coder Next Oq2: Semantic (S:50), Authority (A:0), Popularity (P:16), Recency (R:98), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

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Deployment Guide

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Technical Deep Dive

Qwen3-Coder-Next-oQ2

oQ2 mixed-precision MLX quantization produced via oMLX.

  • Quantization: oQ2 (sensitivity-driven, group_size=64)
  • Format: MLX safetensors, loadable with mlx-vlm and mlx-lm

Usage

bash 
pip install mlx-vlm
python3 -m mlx_vlm generate --model bearzi/Qwen3-Coder-Next-oQ2 --prompt "Your prompt here" --max-tokens 512

About oQ

oQ measures per-layer quantization sensitivity through calibration inference and allocates bits where they matter most — critical layers stay at higher precision, tolerant layers compress aggressively. See oMLX docs.

⚠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.

Social Proof

HuggingFace Hub
317Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

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

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--bearzi--qwen3-coder-next-oq2
slug
bearzi--qwen3-coder-next-oq2
source
huggingface
author
bearzi
license
Apache-2.0
tags
mlx, safetensors, qwen3_next, omlx, quantized, oq2, text-generation, conversational, base_model:qwen/qwen3-coder-next, base_model:quantized:qwen/qwen3-coder-next, license:apache-2.0, 2-bit, region:us, oq

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
text-generation

📊 Engagement & Metrics

downloads
317
stars
0
forks
0

Data indexed from public sources. Updated daily.