Instructions to use PNC-user/MiMo_V2.5_coder_Q2-pnc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PNC-user/MiMo_V2.5_coder_Q2-pnc with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="PNC-user/MiMo_V2.5_coder_Q2-pnc",
	filename="MiMo-V2.5-coder-Q2-00001-of-00016.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 PNC-user/MiMo_V2.5_coder_Q2-pnc 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 PNC-user/MiMo_V2.5_coder_Q2-pnc
# Run inference directly in the terminal:
llama cli -hf PNC-user/MiMo_V2.5_coder_Q2-pnc

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PNC-user/MiMo_V2.5_coder_Q2-pnc
# Run inference directly in the terminal:
llama cli -hf PNC-user/MiMo_V2.5_coder_Q2-pnc

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 PNC-user/MiMo_V2.5_coder_Q2-pnc
# Run inference directly in the terminal:
./llama-cli -hf PNC-user/MiMo_V2.5_coder_Q2-pnc

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 PNC-user/MiMo_V2.5_coder_Q2-pnc
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PNC-user/MiMo_V2.5_coder_Q2-pnc

Use Docker

docker model run hf.co/PNC-user/MiMo_V2.5_coder_Q2-pnc

LM Studio
Jan

vLLM

How to use PNC-user/MiMo_V2.5_coder_Q2-pnc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PNC-user/MiMo_V2.5_coder_Q2-pnc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PNC-user/MiMo_V2.5_coder_Q2-pnc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PNC-user/MiMo_V2.5_coder_Q2-pnc

Ollama
How to use PNC-user/MiMo_V2.5_coder_Q2-pnc with Ollama:
```
ollama run hf.co/PNC-user/MiMo_V2.5_coder_Q2-pnc
```

Unsloth Studio

How to use PNC-user/MiMo_V2.5_coder_Q2-pnc 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 PNC-user/MiMo_V2.5_coder_Q2-pnc 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 PNC-user/MiMo_V2.5_coder_Q2-pnc to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for PNC-user/MiMo_V2.5_coder_Q2-pnc to start chatting

How to use PNC-user/MiMo_V2.5_coder_Q2-pnc with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PNC-user/MiMo_V2.5_coder_Q2-pnc

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": "PNC-user/MiMo_V2.5_coder_Q2-pnc"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use PNC-user/MiMo_V2.5_coder_Q2-pnc with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PNC-user/MiMo_V2.5_coder_Q2-pnc

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 PNC-user/MiMo_V2.5_coder_Q2-pnc

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use PNC-user/MiMo_V2.5_coder_Q2-pnc with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PNC-user/MiMo_V2.5_coder_Q2-pnc

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 "PNC-user/MiMo_V2.5_coder_Q2-pnc" \
  --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 PNC-user/MiMo_V2.5_coder_Q2-pnc with Docker Model Runner:
```
docker model run hf.co/PNC-user/MiMo_V2.5_coder_Q2-pnc
```

Lemonade

How to use PNC-user/MiMo_V2.5_coder_Q2-pnc with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PNC-user/MiMo_V2.5_coder_Q2-pnc

Run and chat with the model

lemonade run user.MiMo_V2.5_coder_Q2-pnc-{{QUANT_TAG}}

List all available models

lemonade list

MiMo_V2.5_coder_Q2-pnc / run-server.sh

PNC-user

Duplicate from jedisct1/MiMo-V2.5-coder-Q2

22f27d1 26 days ago

Raw

History Blame Contribute Delete

1.55 kB

	#!/usr/bin/env bash
	set -euo pipefail

	SCRIPT_DIR=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)
	LLAMA_SERVER=${LLAMA_SERVER:-llama-server}

	if ! command -v "$LLAMA_SERVER" >/dev/null 2>&1; then
	echo "llama-server was not found. Install llama.cpp or set LLAMA_SERVER=/path/to/llama-server." >&2
	exit 1
	fi

	if [[ -n "${MIMO_MODEL:-}" ]]; then
	MODEL=$MIMO_MODEL
	else
	shopt -s nullglob
	CANDIDATES=("$SCRIPT_DIR"/MiMo-V2.5-coder-Q2-00001-of-*.gguf)
	shopt -u nullglob

	if [[ ${#CANDIDATES[@]} -eq 0 ]]; then
	echo "No first GGUF shard found next to run-server.sh." >&2
	exit 1
	fi

	MODEL=${CANDIDATES[0]}
	fi

	ARGS=(
	--model "$MODEL"
	--host "${MIMO_HOST:-127.0.0.1}"
	--port "${MIMO_PORT:-8080}"
	--ctx-size "${MIMO_CTX:-100000}"
	--parallel "${MIMO_PARALLEL:-1}"
	--batch-size "${MIMO_BATCH:-512}"
	--ubatch-size "${MIMO_UBATCH:-128}"
	--threads "${MIMO_THREADS:-12}"
	--threads-batch "${MIMO_THREADS_BATCH:-18}"
	--prio "${MIMO_PRIO:-0}"
	--poll "${MIMO_POLL:-80}"
	--flash-attn on
	--jinja
	--fit "${MIMO_FIT:-on}"
	--fit-target "${MIMO_FIT_TARGET:-4096}"
	--fit-ctx "${MIMO_FIT_CTX:-100000}"
	--gpu-layers "${MIMO_GPU_LAYERS:-auto}"
	--cache-type-k "${MIMO_CACHE_K:-f16}"
	--cache-type-v "${MIMO_CACHE_V:-f16}"
	--reasoning "${MIMO_REASONING:-off}"
	)

	if [[ "${MIMO_CPU_MOE:-0}" == "1" ]]; then
	ARGS+=(--cpu-moe)
	fi

	if [[ -n "${MIMO_DEVICE:-}" ]]; then
	ARGS+=(--device "$MIMO_DEVICE")
	fi

	if [[ -n "${MIMO_TOOLS:-}" ]]; then
	ARGS+=(--tools "$MIMO_TOOLS")
	fi

	exec "$LLAMA_SERVER" "${ARGS[@]}" "$@"