Qwen/Qwen3.8-Flash-Next
Image-Text-to-Text • 180B • Updated • 847k • • 5.71k
akio
xcywzy
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Recent Activity
new activity 6 days ago
ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF:GSQ-RCO-GGUF liked a model 6 days ago
Qwen/Qwen3.8-Flash-Next liked a model 13 days ago
peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-GGUF-MTPOrganizations
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GSQ-RCO-GGUF
2
#12 opened 9 days ago
by 
goodasdgood
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model 6 days ago
liked a
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Model tries too hard and goes wrong.
🤯👍 13
2
#4 opened 15 days ago
by 
Werewolfke
Qwen3.8-Flash-Next
👍 1
#1 opened 14 days ago
by xcywzy
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where my ggufs at
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27
#1 opened about 1 month ago
by 
GGUFGuy
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model about 1 month ago
Requesting UD Q3_K_XL
👍 1
2
#1 opened about 1 month ago
by xcywzy
reacted to eaddario's post with 🔥 about 1 month ago
Post
2802
Experimental global target bits‑per‑weight quantization of Qwen/Qwen3.8-27B, including MTP tensors in imatrix file.
Unlike standard llama.cpp quantization that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach automatically optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global size target.
Key Advantages:
- VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM).
- Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs.
- imatrix includes data for MTP/NextN layers.
Full benchmarks (PPL, KLD, ARC, GPQA, MMLU, etc.) and methodology in the model's card.
eaddario/Qwen3.8-27B-GGUF
Unlike standard llama.cpp quantization that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach automatically optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global size target.
Key Advantages:
- VRAM Maximization: Can generate high quality models sized exactly to fit hardware constraints (e.g., fitting the model into exactly 24GB VRAM).
- Data-Driven Precision: Quantization mix is determined by actual weight error sensitivity rather than hardcoded rules, often yielding better PPL/KLD size trade-offs.
- imatrix includes data for MTP/NextN layers.
Full benchmarks (PPL, KLD, ARC, GPQA, MMLU, etc.) and methodology in the model's card.
eaddario/Qwen3.8-27B-GGUF
liked 6
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