Instructions to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF",
	filename="mimo-v25-pro-dflash-draft-bf16-rope5m-vscale-rope64.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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

Use Docker

docker model run hf.co/ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

LM Studio
Jan

vLLM

How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-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": "ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

Ollama
How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16
```

Unsloth Studio

How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF to start chatting

How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

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": "ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

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 ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

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 "ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16" \
  --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"

Docker Model Runner
How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16
```

Lemonade

How to use ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF:BF16

Run and chat with the model

lemonade run user.MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF-BF16

List all available models

lemonade list

MiMo-V2.5-Pro DFlash Draft — BF16 GGUF (ik_llama.cpp)

A GGUF conversion of the DFlash block-diffusion draft for MiMo-V2.5-Pro, built for speculative decoding in ik_llama.cpp. This is the validation artifact for ik_llama.cpp PR #2048 (MiMo DFlash draft support).

⚠️ Not a standalone model. This is a 5-layer draft that shares the target's token embedding and output head. It only produces meaningful output when launched as --model-draft alongside a full MiMo-V2.5-Pro target GGUF. Loading it on its own will not generate coherent text.

Files

File	Size	Precision	SHA256
`mimo-v25-pro-dflash-draft-bf16-rope5m-vscale-rope64.gguf`	5.5 GB	BF16	`75294724ae22df8ff2d10a390e631b86bcafb34479a4b9fc8866a2a07f69a183`

Matching target

Pair this draft with a MiMo-V2.5-Pro target GGUF. Validated against:

gghfez/MiMo-V2.5-Pro-ik_llama-unfused-GGUF → MiMo-V2.5-Pro-IQ3_S-unfused.gguf (IQ3_S, ~349 GiB)

Use an unfused target GGUF. A fused-MoE MiMo GGUF produced garbled output with this draft in testing; the unfused tensor layout is required for the draft's captured-layer features to line up.

Usage (ik_llama.cpp)

llama-server \
  -m   MiMo-V2.5-Pro-IQ3_S-unfused.gguf \
  --model-draft mimo-v25-pro-dflash-draft-bf16-rope5m-vscale-rope64.gguf \
  -ngl 999 -ngld 999 \
  -sm graph --max-gpu 4 \
  -fa on -ctk q4_0 -ctv q4_0 -ctkd q4_0 -ctvd q4_0 \
  --spec-type dflash:n_max=1,p_min=0.0,cross_ctx=16 \
  -c 8192 --host 127.0.0.1 --port 8080

--spec-type dflash:... selects the DFlash speculator. n_max=1 was optimal in testing (the draft runs autoregressively, so per-step overhead grows with n_max). cross_ctx=16 is a small ring buffer of recent target positions visible to the draft; larger values collapsed acceptance.
-sm graph --max-gpu 4 (tensor + graph split) was the best multi-GPU profile on 4×H200 NVL. Pinning the draft to one GPU with -devd added overhead and is not recommended under graph split.

Conversion metadata

GGUF key	Value	Note
`dflash-draft.block_count`	5	5-layer draft
`dflash-draft.attention.head_count` / `head_count_kv`	128 / 8	GQA
`dflash-draft.rope.dimension_count`	64	`head_dim 128 × partial_rotary_factor 0.5` (upper half is NoPE)
`dflash-draft.rope.freq_base`	5,000,000	backbone rotary base
`dflash-draft.attention.value_scale`	0.612
`dflash-draft.attention.sliding_window`	1024	all-SWA pattern across the 5 layers
IO contract	`io=shared-target`	`token_embd`/`output` reuse the target's tensors

Validation

4×H200 NVL (NVLink), ik_llama.cpp PR #2048. Draft accepted greedily at n_max=1, cross_ctx=16, q4_0 KV on both target and draft.

Prompt	Draft acceptance	DFlash decode (tok/s)	No-spec decode (tok/s)
`double-link-list` fixture	54.6%	55.6	59.9
`quick-sort` fixture	60.4%	59.4	60.8

Conversion notes

Source draft weights: XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash (dflash/ subdir, BF16 drafter).
Vocab/tokenizer: shared from the MiMo-V2.5-Pro target (the draft carries no embeddings of its own).
Converter: convert_hf_to_gguf.py on the filament/mimo-dflash-draft-convert branch (ik_llama.cpp PR #2048), DFlashDraftModel path, --outtype bf16.

python convert_hf_to_gguf.py dflash \
  --target-model-dir . \
  --outtype bf16 \
  --outfile mimo-v25-pro-dflash-draft-bf16-rope5m-vscale-rope64.gguf

Downloads last month: 584

GGUF

Model size

3B params

Architecture

dflash-draft

Hardware compatibility

16-bit

Model tree for ji-farthing/MiMo-V2.5-Pro-DFlash-draft-ik-llama-GGUF

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XiaomiMiMo/MiMo-V2.5-Pro

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