Instructions to use bigavlog/qwen2.5-7b-instruct-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bigavlog/qwen2.5-7b-instruct-quantized 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 bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf bigavlog/qwen2.5-7b-instruct-quantized: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 bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf bigavlog/qwen2.5-7b-instruct-quantized: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 bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS

Use Docker

docker model run hf.co/bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS

LM Studio
Jan
Ollama
How to use bigavlog/qwen2.5-7b-instruct-quantized with Ollama:
```
ollama run hf.co/bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS
```
Unsloth Desktop

How to use bigavlog/qwen2.5-7b-instruct-quantized with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bigavlog/qwen2.5-7b-instruct-quantized: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": "bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bigavlog/qwen2.5-7b-instruct-quantized with Docker Model Runner:
```
docker model run hf.co/bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS
```

Lemonade

How to use bigavlog/qwen2.5-7b-instruct-quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS

Run and chat with the model

lemonade run user.qwen2.5-7b-instruct-quantized-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use bigavlog/qwen2.5-7b-instruct-quantized with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bigavlog/qwen2.5-7b-instruct-quantized: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 bigavlog/qwen2.5-7b-instruct-quantized:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bigavlog/qwen2.5-7b-instruct-quantized with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bigavlog/qwen2.5-7b-instruct-quantized: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 "bigavlog/qwen2.5-7b-instruct-quantized: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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Qwen2.5-7B-Instruct — GGUF Quantization Study

This repository contains four GGUF quantizations of Qwen2.5-7B-Instruct, created with llama.cpp.

The experiment compares K-Quants and I-Quants and measures the effect of importance-matrix (imatrix) calibration.

Quantized Models

Variant	Size	BPW
Q4_K_M	4.36 GiB	4.91
Q4_K_M + imatrix	4.36 GiB	4.91
IQ4_XS	3.95 GiB	4.46
IQ4_XS + imatrix	3.92 GiB	4.43

Evaluation

Held-out perplexity

Variant	PPL
Q4_K_M	5.2194 ± 0.5684
Q4_K_M + imatrix	5.1698 ± 0.5643
IQ4_XS	5.4115 ± 0.6006
IQ4_XS + imatrix	5.3273 ± 0.5908

Lower is better.

Generation throughput

Measured locally on an Apple Silicon M5 MacBook Pro with 16 GB unified memory using llama.cpp and Metal.

Variant	Generation tok/s
Q4_K_M	26.37
Q4_K_M + imatrix	26.20
IQ4_XS	28.60
IQ4_XS + imatrix	29.10

Task evaluation

Six held-out tasks were evaluated for each model across coding, mathematics, reasoning, and structured JSON generation.

Variant	Score
Q4_K_M	18/18
Q4_K_M + imatrix	18/18
IQ4_XS	16/18
IQ4_XS + imatrix	17/18

Key Findings

In this experiment:

Q4_K_M produced the best measured perplexity and a perfect task score.
Adding the imatrix slightly improved Q4_K_M perplexity without changing its nominal storage size.
IQ4_XS reduced model size and delivered higher generation throughput, but showed a measurable quality trade-off.
Adding the imatrix to IQ4_XS partially recovered the perplexity gap while retaining the smaller model size.
The IQ4_XS + imatrix variant was the strongest size/quality/performance compromise observed in this experiment.

These findings are specific to this model, calibration corpus, hardware, and evaluation methodology. They should not be interpreted as a universal ranking of quantization formats.

Calibration

A custom technical calibration corpus was created covering programming, algorithms, mathematics, machine learning, LLMs, quantization, databases, distributed systems, performance engineering, RAG, evaluation, and reproducibility.

Approximately 13,000 tokens from this corpus were used to generate the importance matrix because the full workload exceeded the available Metal working set on the 16 GB system.

The importance matrix was generated using:

Context: 512
Batch size: 512
Micro-batch size: 64
GPU layers: 20

Hardware

Apple Silicon M5
16 GB unified memory
macOS
llama.cpp with Metal acceleration

Usage

These GGUF files can be used with llama.cpp-compatible applications.

Example:

llama-cli \
  -m Qwen2.5-7B-Instruct-Q4_K_M.gguf \
  -ngl 99 \
  -c 2048

Replace the filename with any of the other quantized variants to compare them.

Reproducibility

The complete experiment documentation, calibration data, evaluation data, scripts, and measured results are available in the companion GitHub repository:

https://github.com/bigavloglive/qwen2.5-7b-quantization

The GitHub repository intentionally excludes the large GGUF files.

Limitations

The calibration corpus is custom rather than a standardized benchmark.
Only approximately 13,000 calibration tokens were used for imatrix generation.
The task evaluation contains only six prompts per model.
Perplexity uncertainty intervals overlap.
Throughput can vary with system state and caching.
This study evaluates only approximately 4-bit quantization formats.
Qwen2.5-7B-Instruct is instruction-tuned, so perplexity alone does not fully measure instruction-following quality.

Base Model and License

The base model is:

Qwen/Qwen2.5-7B-Instruct

The original model is released under the Apache 2.0 license.

Please review the original model card and license for the applicable terms and attribution requirements.

This repository contains quantized derivatives of the original model; it does not claim ownership of the underlying Qwen model.

Experiment Repository

GitHub:

https://github.com/bigavloglive/qwen2.5-7b-quantization

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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