Instructions to use gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit"

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 gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit

Run Hermes

hermes

OpenClaw new

How to use gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit"

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 "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MiniCPM5-1B-Claude-Opus-Fable5-Thinking · MLX 8-bit

An 8-bit MLX build of GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking, repacked so it runs natively and fast on Apple Silicon through mlx-lm. Same weights — just quantized to 8-bit and converted to MLX. Nothing else touched.

Why 8-bit instead of 4-bit

It's a 1B model. Squeeze one this small down to 4-bit and it starts making the kind of careless mistakes that make you close the tab — and you'd only save about 400 MB doing it. 8-bit comes out to ~1.1 GB, stays effectively lossless, and still decodes north of 100 tok/s on an M4 Pro. For a model whose whole appeal is being tiny and quick on-device, that's the trade that actually makes sense.

Running it

from mlx_lm import load, generate

model, tok = load("gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit")

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Reverse a string in Python."}],
    add_generation_prompt=True,
    enable_thinking=True,   # set False to skip the <think> block and get a straight answer
)
print(generate(model, tok, prompt, max_tokens=512))

It's a thinking model, Qwen3-style <think>…</think>. Leave enable_thinking=True and you get the reasoning before the answer; flip it to False and the chat template drops in an empty think block so you just get the reply. 128k context, plain Llama architecture underneath, so any recent mlx-lm loads it — no trust_remote_code.

What to expect

One billion parameters, so keep your expectations honest. It's genuinely pleasant for quick on-device chat, drafting, small coding nudges, and watching a little model reason out loud — and it will also state wrong things with complete confidence. Great as the always-on local assistant you keep in a terminal or wired into a Telegram bot; not something to put on the critical path.

Credits

Base pretrain — openbmb/MiniCPM5-1B
Fine-tune (Opus Fable5, thinking mode) — GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking
8-bit MLX conversion — mlx-lm

License is Apache-2.0, inherited from the base.

Downloads last month: -

Safetensors

Model size

0.3B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit

Model tree for gtoxlili/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-MLX-8bit

Base model

openbmb/MiniCPM5-1B

Finetuned

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

Quantized

(4)

this model