AI & ML interests

Memory-fit quantization · role-aware GGUF builds · running frontier LLMs on small hardware · MoE expert placement

Recent Activity

Organization Card

Frontier models. Small hardware. No compromise.

Pollard Weights builds a model measured to fit your target device — phone to wafer — and ships it to any runtime: GGUF · MLX · GPTQ · MX · EXL3 · EXLR8 · DRAFT.

Lane What ships Runs on
GGUF full ladder (Q6_K · IQ4_XS · IQ3_S · 2-bit) + the IQ*_KT trellis flagship, mmproj for vision/audio llama.cpp / Ollama / LM Studio (stock); flagship on ik_llama
MLX 2 / 3 / 4 / 5 / 6 / 8-bit, measured allocation Apple Silicon (mlx-lm / mlx-vlm)
GPTQ int2–int8, dynamic per-layer 4/8 mix vLLM / SGLang
MX NVFP4 · MXFP4 · FP8 · MXFP6 E2M3 / E3M2 · measured FP8/MXFP6/FP4 mixes Blackwell / Rubin via vLLM (FP8 also Hopper / Ada)
EXL3 several bpw, smoothed + Calib 3.0 exllamav3
EXLR8 one trellis encode, every bpw a manifest (2.5–6 bpw) GR8 / reference decode
DRAFT speculative-decoding drafter (DFlash / DSpark / EAGLE-3) measured against the quantized target llama.cpp / vLLM / SGLang / MLX
  • 🧩 Every lane ships the whole model — vision and audio towers, projectors, processors. Never silently text-only.
  • 🛠️ Tool-call aware on every build: calibration, gate and card.
  • 📁 One repo per model, every lane inside: GGUF at the root, MLX/ GPTQ/ MX/ EXL3/ EXLR8/ DRAFT/ folders.
  • 📊 Measured numbers on every card: KLD, top-1, PPL and tool calls per rung.

datasets 0

None public yet