Instructions to use Baekpica/MiMo-V2.6-Flash-RL-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 Baekpica/MiMo-V2.6-Flash-RL-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 Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

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 Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

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 Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

Use Docker

docker model run hf.co/Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Baekpica/MiMo-V2.6-Flash-RL-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Baekpica/MiMo-V2.6-Flash-RL-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": "Baekpica/MiMo-V2.6-Flash-RL-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

Ollama
How to use Baekpica/MiMo-V2.6-Flash-RL-GGUF with Ollama:
```
ollama run hf.co/Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0
```
Unsloth Desktop

How to use Baekpica/MiMo-V2.6-Flash-RL-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Baekpica/MiMo-V2.6-Flash-RL-GGUF with Docker Model Runner:
```
docker model run hf.co/Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0
```

Lemonade

How to use Baekpica/MiMo-V2.6-Flash-RL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

Run and chat with the model

lemonade run user.MiMo-V2.6-Flash-RL-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Baekpica/MiMo-V2.6-Flash-RL-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 Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

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 Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Baekpica/MiMo-V2.6-Flash-RL-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0

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 "Baekpica/MiMo-V2.6-Flash-RL-GGUF:Q8_0" \
  --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"

MiMo-V2.6-Flash-RL-GGUF / README.md

Baekpica

Publish original-representation MXFP4 BF16 calibration reference

de01a8a verified 19 days ago

preview code

Raw History Blame

3.47 kB

	---
	license: mit
	base_model: XiaomiMiMo/MiMo-V2.6-Flash-RL
	base_model_relation: quantized
	library_name: gguf
	pipeline_tag: text-generation
	tags:
	- gguf
	- mimo_v2
	- mxfp4
	- calibration
	---

	# MiMo-V2.6-Flash-RL GGUF — calibration reference

	This repository holds intermediate artifacts actually generated while building [MiMo-V2.6-Flash-RL Mixed-Quant GGUF](https://huggingface.co/Baekpica/MiMo-V2.6-Flash-RL-Mixed-Quant-GGUF). The final compact mixed variant belongs in that separate repository.

	The source is [XiaomiMiMo/MiMo-V2.6-Flash-RL](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL), pinned to [`3b38d063180c3e4aed9691fdc735f3d10b266ee4`](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/tree/3b38d063180c3e4aed9691fdc735f3d10b266ee4).

	## Reference representation

	The four `MXFP4-BF16` shards preserve the original routed experts through an exact MXFP4 repack and expand source FP8 dense matrices to BF16. Control tensors are stored as F32. This is not a full-BF16 source checkpoint or a Q8_0 baseline. It provides original-checkpoint values for importance-matrix collection without recalibrating from the final IQ2 weights.

	\| Shard \| Bytes \|
	\|---\|---:\|
	\| `MiMo-V2.6-Flash-RL-MXFP4-BF16-00001-of-00004.gguf` \| 44,499,128,352 \|
	\| `MiMo-V2.6-Flash-RL-MXFP4-BF16-00002-of-00004.gguf` \| 44,493,179,840 \|
	\| `MiMo-V2.6-Flash-RL-MXFP4-BF16-00003-of-00004.gguf` \| 44,493,179,840 \|
	\| `MiMo-V2.6-Flash-RL-MXFP4-BF16-00004-of-00004.gguf` \| 41,422,766,592 \|
	\| Total \| 174,908,254,624 \|

	Keep all shards together and open the first shard. The language artifact includes the checkpoint's three embedded MTP blocks; this is not evidence of validated speculative decoding. Multimodal encoders and the separate DFlash model are separate components and are not supplied by these four shards alone.

	## Validation status

	- All 90 downloaded source repository files passed Hub checksum verification.
	- Independent MXFP4 repacking and tensor-parallel QKV ordering checks passed.
	- All 36,096 expert matrices were covered by a deterministic sampled-row audit: 108,257 rows matched the independently repacked source. See `reference-audit.json`; this is not an exhaustive payload comparison.
	- The reference loaded on a B300 GPU and passed a short arithmetic decode check.
	- Original-representation text imatrix collection is running as of 2026-09-22.
	- Final mixed-model quality, multimodal end-to-end behavior, MTP/DFlash execution, and DGX Spark serving remain pending. No throughput or benchmark qualification is claimed here.

	`artifact-manifest.json` records exact file sizes and SHA-256 digests. `SHA256SUMS` can be checked after download:

	```bash
	hf download Baekpica/MiMo-V2.6-Flash-RL-GGUF --local-dir ./MiMo-reference
	cd ./MiMo-reference
	sha256sum -c SHA256SUMS
	```

	## Chat template and reproduction

	`chat_template.jinja` is copied byte for byte from the pinned source. Use the original MiMo tokenizer and special-token mapping. Correct image, audio, and video processing additionally requires the corresponding native encoder and input protocol.

	The conversion uses a local MXFP4 extension to llama.cpp revision `5836771`. Reproduction scripts will accompany the mixed release and private Spark handoff. The reference's approximately 175 GB file size is not the compact DGX Spark target; see the separate mixed model card for that recipe and its current estimates.

	## License

	MIT, inherited from the pinned upstream model.