Instructions to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF 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 agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

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 agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

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 agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

Use Docker

docker model run hf.co/agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF with Ollama:
```
ollama run hf.co/agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M
```
Unsloth Desktop

How to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

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": "agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF with Docker Model Runner:
```
docker model run hf.co/agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M
```

Lemonade

How to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-1.5b-reasoning-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

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 agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use agentbyumer/qwen2.5-1.5b-reasoning-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M

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 "agentbyumer/qwen2.5-1.5b-reasoning-GGUF:Q4_K_M" \
  --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-Reasoning — GGUF

GGUF-quantized version of agentbyumer/qwen2.5-1.5b-reasoning, a QLoRA fine-tune of Qwen2.5-1.5B-Instruct trained on a subset of orca-math-word-problems-200k for step-by-step math word problem solving.

Intended for local/CPU inference via llama.cpp, LM Studio, Ollama, or similar GGUF-compatible runtimes.

For full training details, evaluation notes, and known limitations, see the main model card.

Available Files

File	Quantization
`Qwen2.5-1.5B-Instruct.Q4_K_M.gguf`	Q4_K_M — smaller, faster, lower precision
`Qwen2.5-1.5B-Instruct.Q8_0.gguf`	Q8_0 — larger, higher precision

Usage

llama-cli -hf agentbyumer/qwen2.5-1.5b-reasoning-GGUF --jinja

Training Summary

Base model: unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
Method: QLoRA, 3 epochs, ~8,000-row subset of orca-math-word-problems-200k
License: apache-2.0
Informal testing showed no consistent reasoning improvement over the base model on harder multi-step problems — see the main repo's model card for details.

Downloads last month: 137

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit

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Model tree for agentbyumer/qwen2.5-1.5b-reasoning-GGUF

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit

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qwen2.5-1.5b-reasoning-GGUF

Qwen2.5-1.5B-Reasoning — GGUF

Available Files

Usage

Training Summary

Model tree for agentbyumer/qwen2.5-1.5b-reasoning-GGUF

Dataset used to train agentbyumer/qwen2.5-1.5b-reasoning-GGUF