Instructions to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with Trellis:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Use Docker

docker model run hf.co/PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

LM Studio
Jan

vLLM

How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PollardWeights/Qwen2.5-1.5B-Instruct-Pollard"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PollardWeights/Qwen2.5-1.5B-Instruct-Pollard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Ollama
How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with Ollama:
```
ollama run hf.co/PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S
```
Unsloth Desktop

How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with Docker Model Runner:
```
docker model run hf.co/PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S
```

Lemonade

How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Run and chat with the model

lemonade run user.Qwen2.5-1.5B-Instruct-Pollard-Q4_K_S

List all available models

lemonade list

Hermes Agent

How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PollardWeights/Qwen2.5-1.5B-Instruct-Pollard with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "PollardWeights/Qwen2.5-1.5B-Instruct-Pollard:Q4_K_S" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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.

Downloads last month: 265

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

6-bit

Model tree for PollardWeights/Qwen2.5-1.5B-Instruct-Pollard

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(284)

this model

PollardWeights
/

Qwen2.5-1.5B-Instruct-Pollard

Qwen2.5-1.5B-Instruct — Pollard

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

Model details

Which file should I choose?

Available files

The imatrix win

Download a specific file

How to run

imatrix (calibration)

ARM / AVX

Errata

Credits & license

Model tree for PollardWeights/Qwen2.5-1.5B-Instruct-Pollard