Instructions to use mlx-community/Tiny-Pickle-v3-Coder-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Tiny-Pickle-v3-Coder-4bit 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("mlx-community/Tiny-Pickle-v3-Coder-4bit")

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 mlx-community/Tiny-Pickle-v3-Coder-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Tiny-Pickle-v3-Coder-4bit"

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": "mlx-community/Tiny-Pickle-v3-Coder-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mlx-community/Tiny-Pickle-v3-Coder-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Tiny-Pickle-v3-Coder-4bit"

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 "mlx-community/Tiny-Pickle-v3-Coder-4bit" \
  --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 mlx-community/Tiny-Pickle-v3-Coder-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Tiny-Pickle-v3-Coder-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Tiny-Pickle-v3-Coder-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Tiny-Pickle-v3-Coder-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/Tiny-Pickle-v3-Coder-4bit 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 "mlx-community/Tiny-Pickle-v3-Coder-4bit"

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 mlx-community/Tiny-Pickle-v3-Coder-4bit

Run Hermes

hermes

Tiny-Pickle-v3-Coder-4bit / README.md

vsan

Add M1 Max local performance results

f726e26 verified 1 day ago

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metadata

base_model: vsan/tiny-pickle-v3-coder
library_name: mlx
pipeline_tag: text-generation
license: apache-2.0
tags:
  - mlx
  - qwen3-coder
  - coding
  - software-engineering
  - quantized
  - 4bit
  - apple-silicon
  - moe
  - tiny-pickle

Tiny Pickle v3 Coder — MLX 4-bit

Tiny Pickle v3 Coder is a coding-focused adaptation of Qwen/Qwen3-Coder-30B-A3B-Instruct, converted to MLX and quantized for Apple Silicon.

Model lineage

Base model: Qwen/Qwen3-Coder-30B-A3B-Instruct
LoRA adapter: vsan/tiny-pickle-v3-coder-LoRA
Merged Safetensors: vsan/tiny-pickle-v3-coder
Quantization: MLX affine 4-bit
Group size: 64
Converted directory size: 16G

Installation

pip install -U mlx-lm

Interactive chat

mlx_lm.chat --model mlx-community/Tiny-Pickle-v3-Coder-4bit

Command-line generation

mlx_lm.generate \
  --model mlx-community/Tiny-Pickle-v3-Coder-4bit \
  --prompt "Write a tested Python implementation of an LRU cache." \
  --max-tokens 800

Intended use

Code generation, debugging, code review, implementation planning, test generation, and local software-engineering assistance on Apple Silicon.

Quantization

This release uses MLX 4-bit affine quantization with group size 64. Quantization reduces storage and unified-memory requirements but may alter outputs or reduce quality relative to the merged BF16 model.

Limitations

Tiny Pickle v3 Coder is experimental and has not yet been independently demonstrated to outperform its base model. Generated code may be incorrect, insecure, incomplete, or non-functional and must be reviewed and tested.

Related repositories

LoRA: https://huggingface.co/vsan/tiny-pickle-v3-coder-LoRA
Merged Safetensors: https://huggingface.co/vsan/tiny-pickle-v3-coder
GGUF: https://huggingface.co/vsan/tiny-pickle-v3-coder-GGUF

Local Performance

The following result is a single local inference measurement, not a standardized benchmark.

Property	Result
Hardware	Apple M1 Max
Unified memory	64 GB
Model format	MLX 4-bit affine
Quantization group size	64
Prompt length	118 tokens
Prompt processing speed	119.328 tokens/s
Generated length	748 tokens
Generation speed	63.481 tokens/s
Peak unified memory	17.393 GB

Test prompt

You are reviewing a Python async web crawler. Implement a complete, production-quality crawler that uses asyncio and aiohttp; limits global concurrency to 20; limits each domain to 2 concurrent requests; respects robots.txt; retries HTTP 429 and 5xx responses with exponential backoff and jitter; avoids duplicate URLs; normalizes relative links; restricts crawling to the starting domain; supports cancellation; records failures without stopping the crawl; and includes pytest tests using mocked HTTP responses. Return one self-contained Python module followed by the tests.

Results can vary with the macOS version, MLX-LM version, background processes, context length, sampling configuration, and thermal state.