Instructions to use AshScholar/r1m-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AshScholar/r1m-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AshScholar/r1m-gguf",
	filename="r1m-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use AshScholar/r1m-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AshScholar/r1m-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AshScholar/r1m-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AshScholar/r1m-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AshScholar/r1m-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 AshScholar/r1m-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AshScholar/r1m-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 AshScholar/r1m-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AshScholar/r1m-gguf:Q4_K_M

Use Docker

docker model run hf.co/AshScholar/r1m-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use AshScholar/r1m-gguf with Ollama:
```
ollama run hf.co/AshScholar/r1m-gguf:Q4_K_M
```

Unsloth Studio new

How to use AshScholar/r1m-gguf 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 AshScholar/r1m-gguf 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 AshScholar/r1m-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AshScholar/r1m-gguf to start chatting

Pi new

How to use AshScholar/r1m-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AshScholar/r1m-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AshScholar/r1m-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AshScholar/r1m-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AshScholar/r1m-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 AshScholar/r1m-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use AshScholar/r1m-gguf with Docker Model Runner:
```
docker model run hf.co/AshScholar/r1m-gguf:Q4_K_M
```

Lemonade

How to use AshScholar/r1m-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AshScholar/r1m-gguf:Q4_K_M

Run and chat with the model

lemonade run user.r1m-gguf-Q4_K_M

List all available models

lemonade list

AshScholar/r1m-Q4_K_M-GGUF

This model was converted to GGUF format from AshScholar/r1m using llama.cpp via the ggml.ai's GGUF-my-repo space.

About Model

This model was finetuned from Qwen2.5-1M on data from DeepSeek R1. This model uses CoT, and also decides when or when not to use CoT. Credits to Rombo Org for the curated dataset from R1. r1m has a one million token context and near R1 performance.

Specs

7 billion parameters 1 million token context Utilizes tokens.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo AshScholar/r1m-Q4_K_M-GGUF --hf-file r1m-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo AshScholar/r1m-Q4_K_M-GGUF --hf-file r1m-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo AshScholar/r1m-Q4_K_M-GGUF --hf-file r1m-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo AshScholar/r1m-Q4_K_M-GGUF --hf-file r1m-q4_k_m.gguf -c 2048

Downloads last month: 2

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for AshScholar/r1m-gguf

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct-1M

Quantized

unsloth/Qwen2.5-7B-Instruct-1M-unsloth-bnb-4bit

Finetuned

AshScholar/r1m

Quantized

(1)

this model