Instructions to use djrsystemservices/Ling-3.0-tiny-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use djrsystemservices/Ling-3.0-tiny-oQ8e 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("djrsystemservices/Ling-3.0-tiny-oQ8e")

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 djrsystemservices/Ling-3.0-tiny-oQ8e with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "djrsystemservices/Ling-3.0-tiny-oQ8e"

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": "djrsystemservices/Ling-3.0-tiny-oQ8e"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use djrsystemservices/Ling-3.0-tiny-oQ8e with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "djrsystemservices/Ling-3.0-tiny-oQ8e"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "djrsystemservices/Ling-3.0-tiny-oQ8e"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "djrsystemservices/Ling-3.0-tiny-oQ8e",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use djrsystemservices/Ling-3.0-tiny-oQ8e 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 "djrsystemservices/Ling-3.0-tiny-oQ8e"

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 djrsystemservices/Ling-3.0-tiny-oQ8e

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use djrsystemservices/Ling-3.0-tiny-oQ8e with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "djrsystemservices/Ling-3.0-tiny-oQ8e"

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 "djrsystemservices/Ling-3.0-tiny-oQ8e" \
  --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"

Ling-3.0-tiny-oQ8e / README.md

djrsystemservices

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metadata

library_name: mlx
base_model: inclusionAI/Ling-3.0-tiny
license: mit
pipeline_tag: text-generation
tags:
  - mlx
  - omlx
  - oqe
  - quantized

Ling-3.0-tiny-oQ8e

This is an oQ8e MLX conversion produced with oMLX.

Source and credit

Original local source checkpoint: Ling-3.0-tiny-bf16
Source repository / credit: https://huggingface.co/inclusionAI/Ling-3.0-tiny

Please follow the original model's license, acceptable-use policy, and attribution requirements.

Conversion

Quantization: oQ8e (enhanced=True)
Importance calibration: imatrix, 128 samples × 512 tokens
oMLX version: 0.6.0.dev1

oQe uses an imatrix sensitivity calibration to assign precision selectively, rather than applying a uniform bit-width to every tensor. The generated oq_imatrix_report.json records the resulting quantization allocation.

python run_oqe_conversion.py Ling-3.0-tiny-bf16 --models-dir /path/to/models --oq-level 8.0 --imatrix-samples 128 --imatrix-seq-length 512 --source-repo https://huggingface.co/inclusionAI/Ling-3.0-tiny

Files

model*.safetensors: quantized MLX weights
model.safetensors.index.json: shard index
config.json: model configuration
oq_imatrix_report.json: imatrix calibration and quantization report