Instructions to use ubergarm/MiMo-V2-Flash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/MiMo-V2-Flash-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/MiMo-V2-Flash-GGUF",
	filename="IQ2_KL/MiMo-V2-Flash-IQ2_KL-00001-of-00004.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/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf ubergarm/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/MiMo-V2-Flash-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/MiMo-V2-Flash-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/MiMo-V2-Flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/MiMo-V2-Flash-GGUF:Q2_K

Ollama
How to use ubergarm/MiMo-V2-Flash-GGUF with Ollama:
```
ollama run hf.co/ubergarm/MiMo-V2-Flash-GGUF:Q2_K
```

Unsloth Studio new

How to use ubergarm/MiMo-V2-Flash-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/MiMo-V2-Flash-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/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF to start chatting

Pi new

How to use ubergarm/MiMo-V2-Flash-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/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/MiMo-V2-Flash-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/MiMo-V2-Flash-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/MiMo-V2-Flash-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use ubergarm/MiMo-V2-Flash-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/MiMo-V2-Flash-GGUF:Q2_K
```

Lemonade

How to use ubergarm/MiMo-V2-Flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/MiMo-V2-Flash-GGUF:Q2_K

Run and chat with the model

lemonade run user.MiMo-V2-Flash-GGUF-Q2_K

List all available models

lemonade list

`ik_llama.cpp` imatrix Quantizations of XiaomiMiMo/MiMo-V2-Flash

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.

These first two are just test quants for baseline perplexity comparison:

BF16 575.238 GiB (16.003 BPW)
- Final estimate: PPL over 584 chunks for n_ctx=512 = 9.2268 +/- 0.07375
Q8_0 305.682 GiB (8.504 BPW)
- Final estimate: PPL over 584 chunks for n_ctx=512 = 9.1602 +/- 0.07308

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

IQ5_K 213.151 GiB (5.930 BPW)

Final estimate: PPL over 584 chunks for n_ctx=512 = 9.1730 +/- 0.07317

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Routed Experts Layers [1-47]
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/MiMo-V2-Flash-GGUF/imatrix-MiMo-V2-Flash-BF16.dat \
    /mnt/data/models/ubergarm/MiMo-V2-Flash-GGUF/MiMo-V2-Flash-256x7.2B-BF16-00001-of-00013.gguf \
    /mnt/data/models/ubergarm/MiMo-V2-Flash-GGUF/MiMo-V2-Flash-IQ5_K.gguf \
    IQ5_K \
    128

IQ2_KL 106.422 GiB (2.961 BPW)

Final estimate: PPL over 584 chunks for n_ctx=512 = 12.5123 +/- 0.10685

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Routed Experts Layers [1-47]
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# 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/MiMo-V2-Flash-GGUF/imatrix-MiMo-V2-Flash-BF16.dat \
    /mnt/data/models/ubergarm/MiMo-V2-Flash-GGUF/MiMo-V2-Flash-256x7.2B-BF16-00001-of-00013.gguf \
    /mnt/data/models/ubergarm/MiMo-V2-Flash-GGUF/MiMo-V2-Flash-IQ2_KL.gguf \
    IQ2_KL \
    128

smol-IQ2_KS 82.922 GiB (2.307 BPW)

Final estimate: PPL over 584 chunks for n_ctx=512 = 18.2950 +/- 0.16698

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\..*\.attn_q.*=q8_0
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=q8_0

# First 1 Dense Layers [0]
blk\..*\.ffn_down\.weight=q8_0
blk\..*\.ffn_(gate|up)\.weight=q8_0

# Routed Experts Layers [1-47]
blk\..*\.ffn_down_exps\.weight=iq2_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/MiMo-V2-Flash-GGUF/imatrix-MiMo-V2-Flash-BF16.dat \
    /mnt/data/models/ubergarm/MiMo-V2-Flash-GGUF/MiMo-V2-Flash-256x7.2B-BF16-00001-of-00013.gguf \
    /mnt/data/models/ubergarm/MiMo-V2-Flash-GGUF/MiMo-V2-Flash-smol-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)

# Full 2x GPU offload
./build/bin/llama-server \
    --model "$model" \
    --alias ubergarm/MiMo-V2-Flash-GGUF \
    --ctx-size 32768 \
    -ctk q8_0 -ctv q8_0 \
    -sm graph \
    -smgs \
    -mea 256 \
    -ts 42,48 \
    -ngl 99 \
    -ub 2048 -b 2048 \
    --threads 1 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

# Hybrid CPU + 2 or more GPUs
# using new "-sm graph" 'tensor parallel' feature!
# https://github.com/ikawrakow/ik_llama.cpp/pull/1080
# https://github.com/ikawrakow/ik_llama.cpp/pull/1105
For examples take a look at: https://huggingface.co/ubergarm/GLM-4.7-GGUF#quick-start

# CPU Only
numactl -N "$SOCKET" -m "$SOCKET" \
./build/bin/llama-server \
    --model "$model" \
    --alias ubergarm/MiMo-V2-Flash \
    --ctx-size 65536 \
    --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 \
    --no-display-prompt \
    --log-enable \
    --jinja

NOTE: For tool/agentic use you can bring your own template with --chat-template-file myTemplate.jinja and might need --special etc. NOTE: Experiment with: -ger possibly: https://github.com/ikawrakow/ik_llama.cpp/pull/836

References

Downloads last month: 28

GGUF

Model size

309B params

Architecture

mimo2

Hardware compatibility

2-bit

Model tree for ubergarm/MiMo-V2-Flash-GGUF

Base model

XiaomiMiMo/MiMo-V2-Flash

Quantized

(10)

this model

ik_llama.cpp imatrix Quantizations of XiaomiMiMo/MiMo-V2-Flash

Big Thanks

Quant Collection

IQ5_K 213.151 GiB (5.930 BPW)

IQ2_KL 106.422 GiB (2.961 BPW)

smol-IQ2_KS 82.922 GiB (2.307 BPW)

Quick Start

References

Model tree for ubergarm/MiMo-V2-Flash-GGUF

`ik_llama.cpp` imatrix Quantizations of XiaomiMiMo/MiMo-V2-Flash