Instructions to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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("AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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 "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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": "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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 "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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 "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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 AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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 "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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 "AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-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"

Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit / README.md

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metadata

license: apache-2.0
base_model: AnkitAI/Parable-Qwen3-4B-Claude-Fable-5
tags:
  - mlx
  - apple-silicon
  - qwen3
  - agentic
  - coding
  - lora
library_name: mlx
pipeline_tag: text-generation

Parable-Qwen3-4B-Claude-Fable-5 — MLX 4-bit

Apple Silicon build of Parable-Qwen3-4B-Claude-Fable-5, a Qwen3-4B fine-tuned on execution-verified agent traces.

2.1 GB, 4.501 bits per weight. Runs on any M-series Mac with room to spare.

Use it

pip install mlx-lm
mlx_lm.generate --model AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit \
  --prompt "Write a Python function that retries an HTTP call with backoff."

Or in Python:

from mlx_lm import load, generate
model, tokenizer = load("AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-MLX-4bit")
print(generate(model, tokenizer, prompt="...", max_tokens=512))

What it is

Same weights as the source model, quantised to 4-bit for MLX. The recipe behind it is v3.1: LoRA on agent traces plus a replay mix, completion-only loss, two seeds souped, then merged into the base at scale 0.6 to limit drift.

Measured on the 4B, base against tuned in one session on one harness:

	base	v3.1
HumanEval+	0.616	0.683
MBPP+	0.603	0.638

Those numbers are from the full-precision model. Quantisation to 4 bits costs some accuracy; they are the ceiling, not a promise for this build.

Other formats

GGUF — llama.cpp, LM Studio, Ollama
safetensors — transformers

Apache-2.0, same as the base.