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

Run Pi

# Start Pi in your project directory:
pi

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

Atomic Chat

OpenClaw

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"

vsan commited on Aug 6

Commit

f726e26

verified ·

1 Parent(s): 05cc653

Add M1 Max local performance results

Browse files

Files changed (1) hide show

README.md +22 -0

README.md CHANGED Viewed

@@ -73,3 +73,25 @@ insecure, incomplete, or non-functional and must be reviewed and tested.
 - 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

 - 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.