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

Libraries

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF",
	filename="MiniCPM5-1B-Claude-Opus-Fable5-Thinking-F16.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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M

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

Unsloth Studio

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF 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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF 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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF: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": "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-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 GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF with OpenClaw:

Start the llama.cpp server

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

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

Lemonade

How to use GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF

📢 V2.0 is available — We have released an updated model with enhanced tool-calling capabilities. Welcome to try the new version:

Transformers: MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking

GGUF: MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF

GGUF quantizations of MiniCPM5-1B-Claude-Opus-Fable5-Thinking for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes.

中文说明

This repository provides local-deployment builds of a 1B Thinking model fine-tuned on Fable 5 data atop openbmb/MiniCPM5-1B. The GGUF files embed MiniCPM5's native chat template for llama.cpp-compatible runtimes.

Transformers checkpoint: MiniCPM5-1B-Claude-Opus-Fable5-Thinking

Files

File	Quant	Size	Notes
`MiniCPM5-1B-Claude-Opus-Fable5-Thinking-Q4_K_M.gguf`	Q4_K_M	~657 MB	smallest footprint
`MiniCPM5-1B-Claude-Opus-Fable5-Thinking-Q5_K_M.gguf`	Q5_K_M	~751 MB	balanced quality / size
`MiniCPM5-1B-Claude-Opus-Fable5-Thinking-Q8_0.gguf`	Q8_0	~1.1 GB	recommended default
`MiniCPM5-1B-Claude-Opus-Fable5-Thinking-F16.gguf`	F16	~2.1 GB	full-precision conversion base

Q8_0 is the recommended default quant for this 1B model.

Quick start

llama.cpp (`llama-cli`)

llama-cli \
  -m MiniCPM5-1B-Claude-Opus-Fable5-Thinking-Q8_0.gguf \
  -p "Write a Python function to merge two sorted lists." \
  -n 512 \
  --temp 0.9 --top-p 0.95 \
  -c 8192

The model supports up to 128K tokens (131,072) per config.json. Set -c according to your available VRAM/RAM.

llama.cpp server

llama-server \
  -m MiniCPM5-1B-Claude-Opus-Fable5-Thinking-Q8_0.gguf \
  -c 8192 --port 8080

LM Studio / jan / KoboldCpp

Load any .gguf file from this repository. The MiniCPM5 chat template is embedded in the GGUF metadata.

Sampling recommendations

Generation defaults are inherited from MiniCPM5-1B:

Mode	Params
Think (default)	`temperature=0.9, top_p=0.95`
No Think	`temperature=0.7, top_p=0.95`, `enable_thinking=False`

Capabilities

Fable 5 fine-tune — post-trained on Fable 5 data
Coding — code generation, debugging, and software-engineering workflows
Instruction following — more reliable adherence to user prompts and task constraints
Thinking mode — chain-of-thought reasoning; MiniCPM5 chat template baked into the GGUF
Long context — up to 128K tokens (131,072 tokens per upstream config.json)

Limitations

Thinking outputs — the model may emit reasoning blocks before the final answer
1B scale — lightweight local deployment; not frontier-scale
Runtime context — actual usable context depends on your GGUF runtime and hardware limits

Provenance & licensing

Apache-2.0, inherited from MiniCPM5-1B.

Acknowledgements

Base model: OpenBMB / MiniCPM5-1B
Transformers checkpoint: MiniCPM5-1B-Claude-Opus-Fable5-Thinking
Quantization: llama.cpp

Downloads last month: 68,714

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF

Base model

openbmb/MiniCPM5-1B

Finetuned

GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking

Quantized

(4)

this model

MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF

Files

Quick start

llama.cpp (llama-cli)

llama.cpp server

LM Studio / jan / KoboldCpp

Sampling recommendations

Capabilities

Limitations

Provenance & licensing

Acknowledgements

Model tree for GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF

llama.cpp (`llama-cli`)