Qwen2.5-1.5B-Instruct — Pollard

Pollard shrank this model: 3.08 GB (f16) → 0.94 GB — 69% smaller, 3.3× down.

The smallest rung here; larger, higher-fidelity rungs are listed below.

format this model's size

f16 3.08 GB

Q8_0 ~1.63 GB

Q6_K ~1.26 GB

Q4_K_M ~0.89 GB

PollardMix (this repo's Q4_K_S) 0.94 GB

format	this model's size
f16	3.08 GB
Q8_0	~1.63 GB
Q6_K	~1.26 GB
Q4_K_M	~0.89 GB
PollardMix (this repo's Q4_K_S)	0.94 GB

Pollard builds of Qwen/Qwen2.5-1.5B-Instruct made with Pollard Weights — a ladder of measured-allocation quants (bits placed by per-layer sensitivity, not a uniform crush).

Standard GGUF — runs in stock llama.cpp / ik_llama.cpp, Ollama, LM Studio. Trellis (IQ*_KT) files need ik_llama.cpp; the K-quants run anywhere.

Model details


Parameter count	~1.5B
Architecture	`qwen2`
Input support	text
imatrix	yes — see calibration
Perplexity measured	yes — table below

Which file should I choose?

Every rung is the same weights, sized to a different RAM budget by the measured allocation. Pick the largest one that fits your machine with room for context:

~3 GB RAM / VRAM → Q6_K (1.27 GB).
~3 GB RAM / VRAM → Q5_K_M (1.13 GB).
~3 GB RAM / VRAM → Q4_K_S (0.94 GB).

Available files

file	PPL	size	tok/s	Mean KLD	notes
`Qwen2.5-1.5B-Instruct-Pollard-Q4_K_S.gguf`	0.0315	0.94 GB	95.4	—	Q4_K_S
`Qwen2.5-1.5B-Instruct-Pollard-Q5_K_M.gguf`	0.0092	1.13 GB	73.1	—	Q5_K_M
`Qwen2.5-1.5B-Instruct-Pollard-Q6_K.gguf`	0.0050	1.27 GB	73.2	—	Q6_K

tok/s is hardware-specific; the machine it was measured on is stated in the errata.

The imatrix win

Held-out KL-divergence vs the full f16 model (lower = closer), Q4_K_M at matched size:

build	size	mean KL
Q4_K_M with imatrix	0.99 GB	0.0266
Q4_K_M without imatrix	0.99 GB	0.0448

Same bits, ~40% lower KL — that is the entire reason these are imatrix builds.

Download a specific file

pip install -U "huggingface_hub[cli]"
hf download PollardWeights/Qwen2.5-1.5B-Instruct-Pollard \
  --include "Qwen2.5-1.5B-Instruct-Pollard-Q4_K_S.gguf" --local-dir ./

How to run

These are standard GGUF and run with llama.cpp:

llama-server -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

or from a local file:

llama-cli    -m Qwen2.5-1.5B-Instruct-Pollard-Q4_K_S.gguf -ngl 99 -p "Explain why the sky is blue."
llama-server -m Qwen2.5-1.5B-Instruct-Pollard-Q4_K_S.gguf -ngl 99      # OpenAI-compatible API + web UI at :8080

They also work in anything built on llama.cpp — LM Studio, koboldcpp, Jan, ramalama, Ollama (ollama run hf.co/PollardWeights/Qwen2.5-1.5B-Instruct-Pollard).

imatrix (calibration)

The importance matrix (Qwen2.5-1.5B-Instruct-Pollard.imatrix, included) was computed on a mixed-domain corpus so the matrix sees every register the model serves.

ARM / AVX

llama.cpp repacks weights into an interleaved layout at load time for faster inference on ARM and AVX machines — no special file needed, online repacking covers these quants. The old Q4_0_4_4/4_8/8_8 variants are not required.

Errata

Trellis (IQ*_KT) quants need ik_llama.cpp to build/run; K-quants run in any recent llama.cpp.
Measured allocation places bits by per-layer sensitivity under a size budget.
Single machine; replication invited.

Credits & license

Base model: Qwen/Qwen2.5-1.5B-Instruct
Quantization tooling: llama.cpp (ggml-org)
Method + tooling: Pollard Weights — measure first, no claim before a number.
License: apache-2.0, inherited from the base model.

Built with Pollard Weights — frontier models, small hardware, no compromise.

PollardWeights/Qwen2.5-1.5B-Instruct-Pollard