Instructions to use ubergarm/MiniMax-M2.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/MiniMax-M2.5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/MiniMax-M2.5-GGUF",
	filename="IQ2_KS/MiniMax-M2.5-IQ2_KS-00001-of-00003.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ubergarm/MiniMax-M2.5-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

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/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

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/MiniMax-M2.5-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/MiniMax-M2.5-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/MiniMax-M2.5-GGUF"
# 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/MiniMax-M2.5-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K

Ollama
How to use ubergarm/MiniMax-M2.5-GGUF with Ollama:
```
ollama run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K
```

Unsloth Studio new

How to use ubergarm/MiniMax-M2.5-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/MiniMax-M2.5-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/MiniMax-M2.5-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/MiniMax-M2.5-GGUF to start chatting

Pi new

How to use ubergarm/MiniMax-M2.5-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ubergarm/MiniMax-M2.5-GGUF:Q2_K

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/MiniMax-M2.5-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/MiniMax-M2.5-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 ubergarm/MiniMax-M2.5-GGUF:Q2_K

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/MiniMax-M2.5-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use ubergarm/MiniMax-M2.5-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/MiniMax-M2.5-GGUF:Q2_K
```

Lemonade

How to use ubergarm/MiniMax-M2.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/MiniMax-M2.5-GGUF:Q2_K

Run and chat with the model

lemonade run user.MiniMax-M2.5-GGUF-Q2_K

List all available models

lemonade list

`ik_llama.cpp` imatrix Quantizations of MiniMaxAI/MiniMax-M2.5

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. (lower is "better")

These two are just a test quants for baseline perplexity comparison and not available for download here:

BF16 426.060 GiB (16.003 BPW)
- PPL over 552 chunks for n_ctx=512 = 8.3386 +/- 0.06651
Q8_0 226.431 GiB (8.505 BPW)
- PPL over 552 chunks for n_ctx=512 = 8.3590 +/- 0.06673

NOTE: The first split file is much smaller on purpose to only contain metadata, its fine!

IQ5_K 157.771 GiB (5.926 BPW)

PPL over 552 chunks for n_ctx=512 = 8.4860 +/- 0.06815

👈 Secret Recipe

custom="
# 61 Repeating Layers [0-61]

# Attention [0-61] GPU
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# Routed Experts Layers [0-61] CPU
blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# Non-Repeating Layers
token_embd\.weight=q8_0
output\.weight=q8_0
"

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/MiniMax-M2.5-GGUF/imatrix-MiniMax-M2.5-BF16.dat \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-256x4.9B-BF16-00001-of-00010.gguf \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-IQ5_K.gguf \
    IQ5_K \
    128

IQ4_NL 121.386 GiB (4.559 BPW)

PPL over 552 chunks for n_ctx=512 = 8.4419 +/- 0.06757

This one is not mainline compat because it uses:

token_embd@iq4_k (instead of mainline q4_K)
output@iq6_k (instead of mainline q6_K`

It gives a nice little boost in perplexity at basically the same size so I opted to use the newer types. It is technically a smol-IQ4_NL but its fine.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 61 Repeating Layers [0-61]

# Attention [0-61] GPU
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# Routed Experts Layers [0-61] CPU
blk\..*\.ffn_down_exps\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_nl

# 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/MiniMax-M2.5-GGUF/imatrix-MiniMax-M2.5-BF16.dat \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-256x4.9B-BF16-00001-of-00010.gguf \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-IQ4_NL.gguf \
    IQ4_NL \
    128

mainline-IQ4_NL 121.234 GiB (4.554 BPW)

PPL over 552 chunks for n_ctx=512 = 8.4528 +/- 0.06759

This one is mainline compat because it uses:

token_embd@q4_K
output@q6_K

This is the one to use for Vulkan, probably Mac, but might need more than 128GB hrm...

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 61 Repeating Layers [0-61]

# Attention [0-61] GPU
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# Routed Experts Layers [0-61] CPU
blk\..*\.ffn_down_exps\.weight=iq4_nl
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_nl

# Non-Repeating Layers
token_embd\.weight=q4_K
output\.weight=q4_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/MiniMax-M2.5-GGUF/imatrix-MiniMax-M2.5-BF16.dat \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-256x4.9B-BF16-00001-of-00010.gguf \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-mainline-IQ4_NL.gguf \
    IQ4_NL \
    128

IQ4_XS 114.842 GiB (4.314 BPW)

PPL over 552 chunks for n_ctx=512 = 8.5702 +/- 0.06901

This is the only quant in this collection that is compatible with mainline llama.cpp. ik_llama.cpp can run all of them. Its technically a smol-IQ4_XS but its fine.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 61 Repeating Layers [0-61]

# Attention [0-61] GPU
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# Routed Experts Layers [0-61] CPU
blk\..*\.ffn_down_exps\.weight=iq4_xs
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_xs

# Non-Repeating Layers
token_embd\.weight=q4_K
output\.weight=q6_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/MiniMax-M2.5-GGUF/imatrix-MiniMax-M2.5-BF16.dat \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-256x4.9B-BF16-00001-of-00010.gguf \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-IQ4_XS.gguf \
    IQ4_XS \
    128

smol-IQ4_KSS 108.671 GiB (4.082 BPW)

PPL over 552 chunks for n_ctx=512 = 8.5815 +/- 0.06888

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 61 Repeating Layers [0-61]

# Attention [0-61] GPU
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# Routed Experts Layers [0-61] CPU
blk\..*\.ffn_down_exps\.weight=iq4_kss
blk\..*\.ffn_(gate|up)_exps\.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/MiniMax-M2.5-GGUF/imatrix-MiniMax-M2.5-BF16.dat \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-256x4.9B-BF16-00001-of-00010.gguf \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-smol-IQ4_KSS.gguf \
    IQ4_KSS \
    128

smol-IQ3_KS 87.237 GiB (3.277 BPW)

PPL over 552 chunks for n_ctx=512 = 8.7539 +/- 0.07075

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 61 Repeating Layers [0-61]

# Attention [0-61] GPU
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# Routed Experts Layers [0-61] CPU
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# 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/MiniMax-M2.5-GGUF/imatrix-MiniMax-M2.5-BF16.dat \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-256x4.9B-BF16-00001-of-00010.gguf \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-smol-IQ3_KS.gguf \
    IQ3_KS \
    128

IQ2_KS 69.800 GiB (2.622 BPW)

PPL over 552 chunks for n_ctx=512 = 9.6827 +/- 0.07972

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 61 Repeating Layers [0-61]

# Attention [0-61] GPU
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# Routed Experts Layers [0-61] CPU
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

# 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/MiniMax-M2.5-GGUF/imatrix-MiniMax-M2.5-BF16.dat \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-256x4.9B-BF16-00001-of-00010.gguf \
    /mnt/data/models/ubergarm/MiniMax-M2.5-GGUF/MiniMax-M2.5-IQ2_KS.gguf \
    IQ2_KS \
    128

Quick Start

# Clone and checkout
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp

# Build for hybrid CPU+CUDA
$ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON
$ cmake --build build --config Release -j $(nproc)

# Download Desired Quant
$ pip install huggingface_hub
$ hf download --local-dir ./MiniMax-M2.5-GGUF/ --include=smol-IQ3_KS/*.gguf ubergarm/MiniMax-M2.5-GGUF

# Hybrid CPU and Single GPU
echo TODO or look at my Step-3.5-Flash for rough example for now using --cpu-moe or --n-cpu-moe XX etc

# Multi GPU Full Offload 128k context 96GB VRAM!!!
model=MiniMax-M2.5-IQ2_KS-00001-of-00003.gguf
_GLIBCXX_REGEX_STATE_LIMIT=1000000 \
CUDA_VISIBLE_DEVICES="0,1" \
./build/bin/llama-sweep-bench \
    --model "$model" \
    --alias ubergarm/MiniMax-M2.5 \
    -khad -ctk q6_0 -ctv q8_0 \
    -c 131072 \
    -ger \
    -sm graph \
    -ngl 99 \
    -ub 4096 -b 4096 \
    -ts 47,48 \
    --threads 1 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

# CPU-Only
numactl -N "$SOCKET" -m "$SOCKET" \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/MiniMax-M2.5 \
    --ctx-size 65536 \
    -ger \
    --merge-qkv \
    -ctk q8_0 -ctv q8_0 \
    -ub 4096 -b 4096 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

My own early testing with opencode suggests that even the smol-IQ3_KS is working okay with tool calling etc!

For tool use you can always bring your own template with --chat-template-file myTemplate.jinja and might need --special etc.

References

Downloads last month: 69

GGUF

Model size

229B params

Architecture

minimax-m2

Hardware compatibility

2-bit

4-bit

Model tree for ubergarm/MiniMax-M2.5-GGUF

Base model

MiniMaxAI/MiniMax-M2.5

Quantized

(69)

this model

ik_llama.cpp imatrix Quantizations of MiniMaxAI/MiniMax-M2.5

Big Thanks

Quant Collection

IQ5_K 157.771 GiB (5.926 BPW)

IQ4_NL 121.386 GiB (4.559 BPW)

mainline-IQ4_NL 121.234 GiB (4.554 BPW)

IQ4_XS 114.842 GiB (4.314 BPW)

smol-IQ4_KSS 108.671 GiB (4.082 BPW)

smol-IQ3_KS 87.237 GiB (3.277 BPW)

IQ2_KS 69.800 GiB (2.622 BPW)

Quick Start

References

Model tree for ubergarm/MiniMax-M2.5-GGUF

`ik_llama.cpp` imatrix Quantizations of MiniMaxAI/MiniMax-M2.5