Instructions to use prithivMLmods/blink-mimo-9b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/blink-mimo-9b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/blink-mimo-9b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/blink-mimo-9b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/blink-mimo-9b-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 prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

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 prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

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 prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/blink-mimo-9b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/blink-mimo-9b-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": "prithivMLmods/blink-mimo-9b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/blink-mimo-9b-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/blink-mimo-9b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/blink-mimo-9b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/blink-mimo-9b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/blink-mimo-9b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/blink-mimo-9b-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/blink-mimo-9b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

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": "prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/blink-mimo-9b-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/blink-mimo-9b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.blink-mimo-9b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/blink-mimo-9b-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 prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

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 prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/blink-mimo-9b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M

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 "prithivMLmods/blink-mimo-9b-GGUF:Q4_K_M" \
  --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"

blink-mimo-9b-GGUF

blink-mimo-9b is a personal research release by thegovind, a LoRA fine-tune (rank 16, 43.3M merged parameters) of MiMo-V2.6-Distill-Qwen-9B built for one-pass typed decisions rather than text generation: given a text/JSON state and up to 512 questions of type choice (≤255 options), noul (yes/no), or score (2–10 ordered levels), it returns FP32-softmax probabilities over only the offered option-letter logits from lm_head in a single forward pass, with the 27-block vision tower left byte-for-byte unchanged and all other language-model tensors bit-identical to the MiMo base. Trained on one pre-registered epoch (123,195 rows, 615 steps) mixing public-source data, program-generated reasoning, decision worlds, and Qwen3.8-27B-authored teacher questions, it lifted the MiMo base's Decision Index 0.1 score from 47.09 (DI-S) to a full-suite score of 56.53 — placing third among locally-compared models behind Jev 1.13.0 (59.51) and the author's own blink-27b, and ahead of larger open entries like Jevfire (27B) and Decider (35B-A3B) — though the authors flag meaningful public-benchmark training overlap (ContractNLI, iSarcasmEval, VAST) that would drop the score to 54.96 if those areas were excluded. On JevBench's public items it solved 48/48 easy, 70/72 standard, and 77/111 hard cases with a hard ECE of 0.136 (less calibrated than the sibling blink-4b's 0.067), and it ships as a Docker-deployable server exposing a Jev-compatible /v1/systemone endpoint with SHA256 weight verification at startup; the MiMo base is declared MIT and its Qwen3.5-9B ancestor is Apache-2.0, but the blink LoRA weights themselves are licensed for non-commercial research and evaluation only.

Model Files

File Name	Quant Type	File Size	File Link	Description
blink-mimo-9b.BF16.gguf	BF16	17.9 GB	Link	Full BF16 weights. Highest quality, largest file size.
blink-mimo-9b.Q3_K_L.gguf	Q3_K_L	4.93 GB	Link	Lower quality but usable, good for low RAM availability.
blink-mimo-9b.Q3_K_M.gguf	Q3_K_M	4.62 GB	Link	Low quality.
blink-mimo-9b.Q4_K_M.gguf	Q4_K_M	5.63 GB	Link	Good quality, default size for most use cases, recommended.
blink-mimo-9b.Q4_K_S.gguf	Q4_K_S	5.35 GB	Link	Slightly lower quality with more space savings, recommended.
blink-mimo-9b.Q5_K_M.gguf	Q5_K_M	6.47 GB	Link	High quality, recommended.
blink-mimo-9b.Q5_K_S.gguf	Q5_K_S	6.31 GB	Link	High quality, recommended.
blink-mimo-9b.Q6_K.gguf	Q6_K	7.36 GB	Link	Very high quality, near perfect, recommended.