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 Settings

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

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

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

IQ4XS quants work great!

by nimishchaudhari - opened Feb 13

Discussion

nimishchaudhari

Feb 13

Made a very pretty solar system simulation quite fast

MiniMax-M2.5-IQ4_XS-00001-of-00004.gguf

Prompt

Tokens: 19
Time: 675.147 ms
Speed: 28.14 t/s
Generation
Tokens: 3369
Time: 296111.681 ms
Speed: 11.38 t/s
Context
n_ctx: 131072
n_past: 3402

Not super disappointing, model is all on RAM and only experts and kv cache on GPUs .

Thanks for these quants, I can write complex agentic workflows, plan them with with GPT OSS 120B and then run the agents to develop overnight 🫥

ubergarm

Owner Feb 13

Great to hear!

Yeah in my limited testing the even smaller smol-IQ3_KS is working with opencode running on CPU-only haha... (fast DDR4-6400MT/s RAM thanks l1t Wendell! lol)

If you have free VRAM you could probably offload a few more routed exps if you don't need the additional kv-cache too..

tarruda

Feb 13

Just finished downloading and it is pretty good! Here are some speed benchmarks:

% llama-bench -m MiniMax-M2.5-IQ4_XS-00001-of-00004.gguf -fa 1 -t 1 -ngl 99 -b 2048 -ub 2048 -d 0,10000                                            
ggml_metal_device_init: tensor API disabled for pre-M5 and pre-A19 devices
ggml_metal_library_init: using embedded metal library
ggml_metal_library_init: loaded in 0.022 sec
ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
ggml_metal_device_init: GPU name:   MTL0
ggml_metal_device_init: GPU family: MTLGPUFamilyApple7  (1007)
ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_device_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_device_init: simdgroup reduction   = true
ggml_metal_device_init: simdgroup matrix mul. = true
ggml_metal_device_init: has unified memory    = true
ggml_metal_device_init: has bfloat            = true
ggml_metal_device_init: has tensor            = false
ggml_metal_device_init: use residency sets    = true
ggml_metal_device_init: use shared buffers    = true
ggml_metal_device_init: recommendedMaxWorkingSetSize  = 134217.73 MB
| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
| minimax-m2 230B.A10B IQ4_XS - 4.25 bpw | 114.84 GiB |   228.69 B | MTL,BLAS   |       1 |     2048 |  1 |           pp512 |        291.93 ± 1.00 |
| minimax-m2 230B.A10B IQ4_XS - 4.25 bpw | 114.84 GiB |   228.69 B | MTL,BLAS   |       1 |     2048 |  1 |           tg128 |         37.54 ± 0.07 |
| minimax-m2 230B.A10B IQ4_XS - 4.25 bpw | 114.84 GiB |   228.69 B | MTL,BLAS   |       1 |     2048 |  1 |  pp512 @ d10000 |        196.77 ± 0.23 |
| minimax-m2 230B.A10B IQ4_XS - 4.25 bpw | 114.84 GiB |   228.69 B | MTL,BLAS   |       1 |     2048 |  1 |  tg128 @ d10000 |         27.81 ± 0.10 |

Step 3.5 flash is still my favorite though, as this one is a very tight fit with 32k context

ubergarm

Owner Feb 14

@tarruda

Yeah it seems like MiniMax-M2.5 takes more kv-cache VRAM for similar context. I didn't look into arch details to explain that yet.

You can try adding -khad -ctk q6_0 -ctk q8_0 to save at least half the kv-cache space for longer context over what looks like unquantized full fp16 you're using. Oh wait if you're on mainline llama.cpp just try -ctk q8_0 -ctv q8_0 and can fit more context.

I posted some speed benchmarks here: https://www.reddit.com/r/LocalLLaMA/comments/1r40o83/comment/o58rg7k/

nimishchaudhari

Feb 14

Great to hear!

Yeah in my limited testing the even smaller smol-IQ3_KS is working with opencode running on CPU-only haha... (fast DDR4-6400MT/s RAM thanks l1t Wendell! lol)

If you have free VRAM you could probably offload a few more routed exps if you don't need the additional kv-cache too..

For me the tokens per second go not more than 15 even if I offload some more exps, so I've learnt to swallow the hard pill of working with slow models.

Speaking of fast ram I have a 7000MT/s 192gb but i7-14700F so I couldn't get it working for faster than 5600 MT. Any advice about how can I get it working on faster speeds with 4 sticks on ddd5?

I am using Minimax m2.5 for open claw for now, it does quite good but misses the mark at times. I am not sure if this implementation works correctly for now but still very impressive for a model that I can host myself 😀

tarruda

Feb 14

Any advice about how can I get it working on faster speeds with 4 sticks on ddd5?

I think memory bandwidth is the bottleneck for token generation. I have good tk/s generation because my mac has 800GB/s memory bandwidth (OTOH it kinda sucks at prompt processing...)

ubergarm

Owner Feb 14

@nimishchaudhari

I get it working on faster speeds with 4 sticks on ddd5?

4x DIMMs is the "verboten" configuration haha.. I'm running 2x48GB on my home AMD9950X rig and able to get DDR5-6400MT/s and clock about 86GiB/s memory bandwidth using mlc (intel memory latency checker). I bottleneck at over 90% of theoretical max TG (active parameters per token divided by memory read bandwidth)

So you could watch some "Actually Hardcore Overclocking" videos on youtube and spend a couple days tweaking your BIOS and stability testing to OC memory, but likely not worth it given possible instability. I have a thread on how I did it, but different mobo/cpu so likely less interesting to you.

@tarruda

Yeah, mac seems to have worse CPU power which is what PP really needs to calculate the kv-cache. That is pretty amazing bandwidth for TG though!

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