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
- Transformers
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
- Pi
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
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 (Commit:
ae62103)
Datasets
- Good prompts: mlabonne/harmless_alpaca (Commit:
02c6a92) - Bad prompts: mlabonne/harmful_behaviors (Commit:
01cead0) - Good evaluation prompts: mlabonne/harmless_alpaca (Commit:
02c6a92) - Bad evaluation prompts: mlabonne/harmful_behaviors (Commit:
01cead0)
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.
Contents of this directory
requirements.txt: The exact versions of all Python packages.config.toml: The exact configuration used, including the RNG seed.GnLOLot--MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking.jsonl: The Optuna study journal containing the history of all trials.SHA256SUMS: Cryptographic hashes for all weight files.reproduce.json: A machine-readable file containing all reproducibility information.
How to reproduce
You can automate this process, including all verification steps, by downloading the
reproduce.jsonfile and runningheretic --reproduce reproduce.json.
- 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.
- Install the exact version of Heretic indicated in the Environment section above, from its original source.
- Install the packages listed in
requirements.txt:pip install -r requirements.txt - Install the correct version of PyTorch:
pip install torch==2.11.0+cu128 --index-url https://download.pytorch.org/whl/cu128 - Place the provided
config.tomlin your working directory. - Run Heretic without any additional arguments:
heretic - Wait for the run to finish, then select trial 186 and export the model.
- 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)
To use the included Optuna study journal
GnLOLot--MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking.jsonl, place it in the checkpoints directory (usuallycheckpoints/) 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.