MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic

Instructions to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Transformers:

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

pipe = pipeline("text-generation", model="chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic")
model = AutoModelForCausalLM.from_pretrained("chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
	filename="MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_K_M.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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Use Docker

docker model run hf.co/chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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": "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

SGLang

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic 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 "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic" \
    --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": "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
		"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 "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic" \
        --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": "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Ollama:
```
ollama run hf.co/chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
```

Unsloth Studio

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic 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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic 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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic to start chatting

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

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": "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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 chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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 "chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-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"

Docker Model Runner
How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Docker Model Runner:
```
docker model run hf.co/chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
```

Lemonade

How to use chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chiakelvin/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_K_M

List all available models

lemonade list

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic / reproduce /README.md

chiakelvin

Duplicate from saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic

331dd33 1 day 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: [GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking) (Commit: [`ae62103`](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking/commit/ae621032de21e5229215e5b882dd5534049b4eda))

	## 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: 186
	- KL divergence: 0.023212
	- Refusals: 3/100

	## System

	- Python: 3.12.13 (CPython, GCC 11.4.0) [System]
	- Operating system: Linux-6.6.122+-x86_64-with-glibc2.35 (x86_64)
	- CPU: Intel(R) Xeon(R) CPU @ 2.00GHz

	### Accelerators

	- CUDA: Detected 1 device(s) (14.56 GB total VRAM)
	- CUDA Version: 12.8
	- Driver Version: 580.82.07
	- Devices:
	- CUDA 0: Tesla T4 (14.56 GB)

	## 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.
	- [`GnLOLot--MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking.jsonl`](GnLOLot--MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking.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. Ensure your system matches the specifications in the System section above. Exact reproducibility is only guaranteed if all aspects of your system are identical to the one the model was originally generated on.
	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 186 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 `GnLOLot--MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking.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.