Instructions to use PollardWeights/MiniCPM5-2B-Pollard-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PollardWeights/MiniCPM5-2B-Pollard-MLX 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("PollardWeights/MiniCPM5-2B-Pollard-MLX")

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 PollardWeights/MiniCPM5-2B-Pollard-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "PollardWeights/MiniCPM5-2B-Pollard-MLX"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "PollardWeights/MiniCPM5-2B-Pollard-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use PollardWeights/MiniCPM5-2B-Pollard-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "PollardWeights/MiniCPM5-2B-Pollard-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "PollardWeights/MiniCPM5-2B-Pollard-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "PollardWeights/MiniCPM5-2B-Pollard-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use PollardWeights/MiniCPM5-2B-Pollard-MLX 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 "PollardWeights/MiniCPM5-2B-Pollard-MLX"

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 PollardWeights/MiniCPM5-2B-Pollard-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PollardWeights/MiniCPM5-2B-Pollard-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "PollardWeights/MiniCPM5-2B-Pollard-MLX"

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 "PollardWeights/MiniCPM5-2B-Pollard-MLX" \
  --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"

MiniCPM5-2B — Pollard (MLX)

Pollard shrank this model for Apple Silicon: 5.0 GB (f16) → 1.6 GB — 68% smaller, 3.1× down.

Three MLX rungs by measured allocation (bits placed per-layer by sensitivity). The mix at the repo root is the recommended default; q8/ and q4/ are the higher- and lower-fidelity rungs.

Pollard MLX builds of openbmb/MiniCPM5-2B made with Pollard Weights. Runs on Apple Silicon via MLX/Metal.

Available rungs

rung	bpw	size	notes	path
q8	8.50	2.5 GB	near-lossless	`q8/`
mix (recommended)	6.48	1.9 GB	measured 4/8 mixed-precision	repo root
q4	5.43	1.6 GB	smallest	`q4/`

PPL / Mean-KLD benchmarking pending — sizes and allocation are final.

Usage

# recommended (the mix, at the repo root):
mlx_lm.generate --model PollardWeights/MiniCPM5-2B-Pollard-MLX --prompt "Explain why the sky is blue."

# a specific rung (q8 or q4) — fetch the subfolder, then point mlx_lm at it:
huggingface-cli download PollardWeights/MiniCPM5-2B-Pollard-MLX --include "q8/*" --local-dir ./mcpm-mlx
mlx_lm.generate --model ./mcpm-mlx/q8 --prompt "Hello"

Errata

MLX quants run on Apple Silicon (Metal) via mlx_lm.
Measured allocation places bits by per-layer sensitivity under a size budget; router/embeddings/attention gates kept high.
Single machine; replication invited.

Built with Pollard Weights — frontier models, small hardware, no compromise.

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Safetensors

Model size

3B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for PollardWeights/MiniCPM5-2B-Pollard-MLX

Base model

openbmb/MiniCPM5-2B

Quantized

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this model

PollardWeights
/

MiniCPM5-2B-Pollard-MLX

MiniCPM5-2B — Pollard (MLX)

Pollard shrank this model for Apple Silicon: 5.0 GB (f16) → 1.6 GB — 68% smaller, 3.1× down.

Available rungs

Usage

Errata

Model tree for PollardWeights/MiniCPM5-2B-Pollard-MLX