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
- llama.cpp
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
- Pi
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"
File size: 3,465 Bytes
de01a8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | ---
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.
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