Instructions to use koshuro/MiniCPM5-1B-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use koshuro/MiniCPM5-1B-heretic 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 koshuro/MiniCPM5-1B-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koshuro/MiniCPM5-1B-heretic:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf koshuro/MiniCPM5-1B-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf koshuro/MiniCPM5-1B-heretic: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 koshuro/MiniCPM5-1B-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf koshuro/MiniCPM5-1B-heretic: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 koshuro/MiniCPM5-1B-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf koshuro/MiniCPM5-1B-heretic:Q4_K_M

Use Docker

docker model run hf.co/koshuro/MiniCPM5-1B-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use koshuro/MiniCPM5-1B-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "koshuro/MiniCPM5-1B-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "koshuro/MiniCPM5-1B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/koshuro/MiniCPM5-1B-heretic:Q4_K_M

Ollama
How to use koshuro/MiniCPM5-1B-heretic with Ollama:
```
ollama run hf.co/koshuro/MiniCPM5-1B-heretic:Q4_K_M
```
Unsloth Desktop

How to use koshuro/MiniCPM5-1B-heretic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koshuro/MiniCPM5-1B-heretic: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": "koshuro/MiniCPM5-1B-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use koshuro/MiniCPM5-1B-heretic with Docker Model Runner:
```
docker model run hf.co/koshuro/MiniCPM5-1B-heretic:Q4_K_M
```

Lemonade

How to use koshuro/MiniCPM5-1B-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull koshuro/MiniCPM5-1B-heretic:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-heretic-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use koshuro/MiniCPM5-1B-heretic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koshuro/MiniCPM5-1B-heretic: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 koshuro/MiniCPM5-1B-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use koshuro/MiniCPM5-1B-heretic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf koshuro/MiniCPM5-1B-heretic: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 "koshuro/MiniCPM5-1B-heretic: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"

MiniCPM5-1B-heretic / reproduce /README.md

koshuro

Upload reproduce/README.md with huggingface_hub

356a7ad verified about 2 months ago

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	# Reproduction guide

	This directory contains the necessary information and assets to reproduce the results obtained during this Heretic run.

	## Models

	- Base model: [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) (Commit: [`4e9de7a`](https://huggingface.co/openbmb/MiniCPM5-1B/commit/4e9de7a0778dc1c362e983e6858f0e77542cbdca))

	## Datasets

	- Good prompts: [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
	- Bad prompts: [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))
	- Good evaluation prompts: [mlabonne/harmless_alpaca](https://huggingface.co/datasets/mlabonne/harmless_alpaca) (Commit: [`02c6a92`](https://huggingface.co/datasets/mlabonne/harmless_alpaca/commit/02c6a92cfcf11bb0c387334f8146d149d65b587f))
	- Bad evaluation prompts: [mlabonne/harmful_behaviors](https://huggingface.co/datasets/mlabonne/harmful_behaviors) (Commit: [`01cead0`](https://huggingface.co/datasets/mlabonne/harmful_behaviors/commit/01cead01398926d81f7c52bdb790ee8cf77ebba7))

	## Selected trial

	- Trial number: 162
	- KL divergence: 0.038307
	- Refusals: 3/100

	## Environment

	- Heretic: v1.4.0 (Origin: PyPI)
	- PyTorch: 2.11.0+cu128
	- Other dependencies: See [`requirements.txt`](requirements.txt).

	## Contents of this directory

	- [`requirements.txt`](requirements.txt): The exact versions of all Python packages.
	- [`config.toml`](config.toml): The exact configuration used, including the RNG seed.
	- [`openbmb--MiniCPM5-1B.jsonl`](openbmb--MiniCPM5-1B.jsonl): The Optuna study journal containing the history of all trials.
	- [`SHA256SUMS`](SHA256SUMS): Cryptographic hashes for all weight files.
	- [`reproduce.json`](reproduce.json): A machine-readable file containing all reproducibility information.

	## How to reproduce

	> [!TIP]
	> You can automate this process, including all verification steps, by downloading the `reproduce.json` file and running
	> `heretic --reproduce reproduce.json`.

	1. Install the exact version of Heretic indicated in the Environment section above, from its original source.
	1. Install the packages listed in `requirements.txt`: `pip install -r requirements.txt`
	1. Install the correct version of PyTorch: `pip install torch==2.11.0+cu128 --index-url https://download.pytorch.org/whl/cu128`
	1. Place the provided `config.toml` in your working directory.
	1. Run Heretic without any additional arguments: `heretic`
	1. Wait for the run to finish, then select trial 162 and export the model.
	1. Verify that the weight files have been exactly reproduced by comparing their SHA-256 hashes against those in `SHA256SUMS`:
	`sha256sum -c SHA256SUMS` (or look at the hashes online if you uploaded to Hugging Face)

	> [!TIP]
	> To use the included Optuna study journal `openbmb--MiniCPM5-1B.jsonl`, place it in the checkpoints directory (usually `checkpoints/`) before running Heretic.
	>
	> This allows you to export other models from the Pareto front, or to run additional trials without having to re-run the stored trials.