SamPurkis/OmniVoice-q8
Updated • 2
Sam Purkis
SamPurkis
AI & ML interests
None yet
Recent Activity
updated a model about 12 hours ago
SamPurkis/OmniVoice-q8 published a model about 12 hours ago
SamPurkis/OmniVoice-q8 updated a model about 17 hours ago
SamPurkis/OmniVoice-f16Organizations
None yet
updated a
model about 12 hours ago
published a
model about 12 hours ago
updated a
model about 17 hours ago
published a
model about 17 hours ago
updated a
model about 1 month ago
published a
model about 1 month ago
reacted to eaddario's post with 👍 3 months ago
Post
3116
Experimental global target bits‑per‑weight quantization of mistralai/Ministral-3-14B-Instruct-2512 and mistralai/Ministral-3-14B-Reasoning-2512
Unlike standard llama.cpp quantizations that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global file 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.
Full benchmarks (PPL, KLD, ARC, MMLU, etc.) and methodology in the models' cards
eaddario/Ministral-3-14B-Instruct-2512-GGUF
eaddario/Ministral-3-14B-Reasoning-2512-GGUF
Unlike standard llama.cpp quantizations that rely on fixed type heuristics (e.g., Q4_K_M), the Target BPW approach optimizes per-tensor precision where it matters the most, and produces high quality models that meet a precise global file 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.
Full benchmarks (PPL, KLD, ARC, MMLU, etc.) and methodology in the models' cards
eaddario/Ministral-3-14B-Instruct-2512-GGUF
eaddario/Ministral-3-14B-Reasoning-2512-GGUF
liked a
Space 3 months ago
published a
model 11 months ago
updated 2
models 11 months ago
published a
model 11 months ago
updated a
model 11 months ago
published a
model 11 months ago
updated a
model 12 months ago
published 2
models 12 months ago
published a
model about 1 year ago
updated a
model about 1 year ago
published a
model about 1 year ago