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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use PollardWeights/Qwen2.5-Coder-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-Coder-1.5B-Instruct-Pollard:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS

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-Coder-1.5B-Instruct-Pollard:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS

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-Coder-1.5B-Instruct-Pollard:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS

Use Docker

docker model run hf.co/PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS

LM Studio
Jan

vLLM

How to use PollardWeights/Qwen2.5-Coder-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-Coder-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-Coder-1.5B-Instruct-Pollard",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS

Ollama
How to use PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard with Ollama:
```
ollama run hf.co/PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS
```

Unsloth Studio

How to use PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard to start chatting

How to use PollardWeights/Qwen2.5-Coder-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-Coder-1.5B-Instruct-Pollard:IQ4_XS

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-Coder-1.5B-Instruct-Pollard:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard with Docker Model Runner:
```
docker model run hf.co/PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:IQ4_XS

Run and chat with the model

lemonade run user.Qwen2.5-Coder-1.5B-Instruct-Pollard-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use PollardWeights/Qwen2.5-Coder-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-Coder-1.5B-Instruct-Pollard:IQ4_XS

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-Coder-1.5B-Instruct-Pollard:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PollardWeights/Qwen2.5-Coder-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-Coder-1.5B-Instruct-Pollard:IQ4_XS

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-Coder-1.5B-Instruct-Pollard:IQ4_XS" \
  --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-Coder-1.5B-Instruct-Pollard / README.md

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metadata

quantized_by: PollardWeights
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
base_model_relation: quantized
license: apache-2.0
language:
  - en
tags:
  - pollard
  - gguf
  - llama.cpp
  - qwen2
  - code
  - code-completion
  - memory-fit
  - conversational

Pollard memory-fit quantizations of Qwen2.5-Coder-1.5B-Instruct by Qwen

Local code completion that fits your box. Built with Pollard Weights — sized to your machine's RAM, not to a bit-width chart. Standard GGUF: runs in any recent llama.cpp (the qwen2 architecture is long-supported) and anything built on it. 70–93 tok/s on an Apple M4, whole model under 1.3 GB.

Original model: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct

Model details


Parameter count	~1.54B (dense)
Architecture	`qwen2` (28 layers)
Input support	text / code
Fill-in-the-middle	yes — Qwen2.5-Coder FIM tokens (see below)
Speculative decoding	no
imatrix	yes — importance-matrix guided (mixed prose + code corpus)
Perplexity / KLD measured	not measured — verified by live code generation + throughput (below)

The sensitive tensors (token embeddings, attention q/k/v/o, norms, output head) keep high precision and the FFN bulk carries the compression — a smarter quant, tuned to how much RAM you actually have. (This is a dense model, so the build uses Pollard's role/depth-aware memory-fit mode + imatrix; the measured-KL knapsack is reserved for MoE models where it demonstrably beats uniform.)

Which file should I choose?

Fastest / smallest → IQ4_XS (0.86 GB, 93 tok/s on M4). Great for a lean completion sidecar.
Balanced (recommended) → Q5_K_M (1.12 GB, 71 tok/s). Best quality-per-byte for everyday completion.
Max fidelity → Q6_K (1.25 GB, 72 tok/s). Near-lossless.

Available files

file	quant	size	M4 tok/s	notes
`…-Pollard-IQ4_XS.gguf`	IQ4_XS	0.86 GB	93.0	fastest / smallest
`…-Pollard-Q5_K_M.gguf`	Q5_K_M	1.12 GB	71.2	balanced — recommended
`…-Pollard-Q6_K.gguf`	Q6_K	1.25 GB	72.5	max fidelity

Prompt format (chat / instruct)

Qwen2.5-Coder uses ChatML:

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Fill-in-the-middle (code completion)

For editor-style completion, use the Qwen2.5-Coder FIM tokens — prefix + suffix, model fills the middle:

<|fim_prefix|>def is_prime(n):
    <|fim_suffix|>
    return True<|fim_middle|>

Repo-level completion is supported too via <|repo_name|> and <|file_sep|> separators.

Download a specific file

pip install -U "huggingface_hub[cli]"
hf download PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard \
  --include "Qwen2.5-Coder-1.5B-Instruct-Pollard-Q5_K_M.gguf" --local-dir ./

How to run

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf PollardWeights/Qwen2.5-Coder-1.5B-Instruct-Pollard:Q5_K_M

or with a local file:

# OpenAI-compatible API + web UI at :8080 — point your editor / continue.dev at it
llama-server -m Qwen2.5-Coder-1.5B-Instruct-Pollard-Q5_K_M.gguf -ngl 99

# one-shot
llama-cli -m Qwen2.5-Coder-1.5B-Instruct-Pollard-Q5_K_M.gguf -ngl 99 -st \
  -p "Write a Python function is_prime(n). Only the function."

# Ollama
ollama create qwen2.5-coder-1.5b-pollard -f Modelfile   # FROM ./…-Q5_K_M.gguf

Also runs in LM Studio, koboldcpp, ramalama, Jan, Text Generation WebUI, LoLLMs — standard GGUF.

Verified

Loaded and generated from on an Apple M4 Mac Mini (16 GB), llama.cpp Metal, before shipping — measure first, no claim before a number. Throughput is in the table above; correctness spot-check (Q5_K_M, "Write a Python function is_prime(n)"):

def is_prime(n):
    if n <= 1:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

imatrix

The importance matrix was computed on a mixed prose + source-code corpus and guides the IQ/K-quant quality. (The base Qwen2.5-Coder builds elsewhere are often quantized without one; these are imatrix-guided.)

ARM / AVX

llama.cpp repacks weights into an interleaved layout at load time for faster ARM/AVX inference — no special file needed; the old Q4_0_4_4/4_8/8_8 variants are not required.

Credits & license

Base model: Qwen2.5-Coder-1.5B-Instruct by the Qwen team, under Apache-2.0. This build inherits that license.
Quantization runtime: llama.cpp (ggml-org).
Method & builder: Pollard Weights — measure first, no claim before a number.