Instructions to use ubergarm/Devstral-2-123B-Instruct-2512-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 ubergarm/Devstral-2-123B-Instruct-2512-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 ubergarm/Devstral-2-123B-Instruct-2512-GGUF
# Run inference directly in the terminal:
llama cli -hf ubergarm/Devstral-2-123B-Instruct-2512-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Devstral-2-123B-Instruct-2512-GGUF
# Run inference directly in the terminal:
llama cli -hf ubergarm/Devstral-2-123B-Instruct-2512-GGUF

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 ubergarm/Devstral-2-123B-Instruct-2512-GGUF
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Devstral-2-123B-Instruct-2512-GGUF

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 ubergarm/Devstral-2-123B-Instruct-2512-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Devstral-2-123B-Instruct-2512-GGUF

Use Docker

docker model run hf.co/ubergarm/Devstral-2-123B-Instruct-2512-GGUF

LM Studio
Jan

vLLM

How to use ubergarm/Devstral-2-123B-Instruct-2512-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Install mistral-common:
pip install --upgrade mistral-common
# Start the vLLM server:
vllm serve "ubergarm/Devstral-2-123B-Instruct-2512-GGUF" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/Devstral-2-123B-Instruct-2512-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Devstral-2-123B-Instruct-2512-GGUF

Ollama
How to use ubergarm/Devstral-2-123B-Instruct-2512-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Devstral-2-123B-Instruct-2512-GGUF
```

Unsloth Studio

How to use ubergarm/Devstral-2-123B-Instruct-2512-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 ubergarm/Devstral-2-123B-Instruct-2512-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 ubergarm/Devstral-2-123B-Instruct-2512-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Devstral-2-123B-Instruct-2512-GGUF to start chatting

How to use ubergarm/Devstral-2-123B-Instruct-2512-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Devstral-2-123B-Instruct-2512-GGUF

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": "ubergarm/Devstral-2-123B-Instruct-2512-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ubergarm/Devstral-2-123B-Instruct-2512-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Devstral-2-123B-Instruct-2512-GGUF

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 "ubergarm/Devstral-2-123B-Instruct-2512-GGUF" \
  --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"

Docker Model Runner
How to use ubergarm/Devstral-2-123B-Instruct-2512-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Devstral-2-123B-Instruct-2512-GGUF
```

Lemonade

How to use ubergarm/Devstral-2-123B-Instruct-2512-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Devstral-2-123B-Instruct-2512-GGUF

Run and chat with the model

lemonade run user.Devstral-2-123B-Instruct-2512-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ubergarm/Devstral-2-123B-Instruct-2512-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 ubergarm/Devstral-2-123B-Instruct-2512-GGUF

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 ubergarm/Devstral-2-123B-Instruct-2512-GGUF

Run Hermes

hermes

Atomic Chat

imatrix Quantization of mistralai/Devstral-2-123B-Instruct-2512

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds for CUDA 12.9. Also check for Windows builds by Thireus here. which have been CUDA 12.8.

These quants provide best in class perplexity for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models! Thanks to huggingface for hosting all these big quants!

Finally, I really appreciate the support from aifoundry.org so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw.

Baseline Q8_0 123.723 GiB (8.500 BPW)
- 3.7919 +/- 0.01980

IQ4_KSS 68.536 GiB (4.709 BPW)

Final estimate: PPL over 594 chunks for n_ctx=512 = 3.8832 +/- 0.02076

👈 Secret Recipe

#!/usr/bin/env bash

custom="
## Attention [0-87]
## Keep qkv the same to allow --merge-qkv
blk\..*\.attn_q.*\.weight=iq6_k
blk\..*\.attn_k.*\.weight=iq6_k
blk\..*\.attn_v.*\.weight=iq6_k
blk\..*\.attn_output.*\.weight=iq6_k

## Dense Layers [0-87]
blk\..*\.ffn_down\.weight=iq4_ks
blk\..*\.ffn_(gate|up)\.weight=iq4_kss

## Non-Repeating layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"""

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Devstral-2-123B-Instruct-2512-GGUF/imatrix-Devstral-2-123B-Instruct-2512-Q8_0.dat \
    /mnt/data/models/ubergarm/Devstral-2-123B-Instruct-2512-GGUF/Devstral-2-123B-Instruct-2512-BF16-00001-of-00006.gguf \
    /mnt/data/models/ubergarm/Devstral-2-123B-Instruct-2512-GGUF/Devstral-2-123B-Instruct-2512-IQ4_KSS.gguf \
    IQ4_KSS \
    128

Quick Start

This is a DENSE model and not an MoE so you will want as many of the 88 layers as possible running on VRAM.

If you can fit the entire model in 2x GPUs, try adding -sm graph for the new ik_llama.cpp tensor parallel implementation.

# Example running full offload on 2x GPUs on ik_llama.cpp
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Devstral-2-123B-Instruct-2512-GGUF \
    -ctk q8_0 -ctv q8_0 \
    --ctx-size 32768 \
    --merge-qkv \
    -ngl 99 \
    --threads 1 \
    --host 127.0.0.1 \
    --port 8080 \
    --parallel 1 \
    --jinja

# Example running Hybrid CPU+GPU(s) on ik_llama.cpp
# adjust the -ngl to fit as many of the 88 layers as possible without OOMing for your desired context
# adjust the threads to match your number of physical cores
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Devstral-2-123B-Instruct-2512-GGUF \
    -ctk q8_0 -ctv q8_0 \
    --ctx-size 32768 \
    --merge-qkv \
    -ngl 20 \
    --threads 16 \
    --host 127.0.0.1 \
    --port 8080 \
    --parallel 1 \
    --no-mmap \
    --jinja

References

Downloads last month: 12

GGUF

Model size

125B params

Architecture

llama

Hardware compatibility

Model tree for ubergarm/Devstral-2-123B-Instruct-2512-GGUF

Base model

mistralai/Devstral-2-123B-Instruct-2512

Quantized

(18)

this model