Instructions to use SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit 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("SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit")

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 SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit"

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": "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit 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 "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit"

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 SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit"

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 "SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit" \
  --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"

Qwen2.5-1.5B-Instruct-mlx-16bit

16-bit MLX conversion of Qwen/Qwen2.5-1.5B-Instruct for Apple Silicon.

Converted by: SirSahOl Source model: Qwen/Qwen2.5-1.5B-Instruct Framework: MLX by Apple Quantization: 16-bit Format: safetensors License: apache-2.0

Quick Start

Installation

pip install mlx-lm

CLI Usage

# Chat interactively
mlx_lm.chat --model SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit

# Generate text
mlx_lm.generate --model SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit --prompt "Your prompt here"

Python Usage

from mlx_lm import load, generate

model, tokenizer = load("SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256)
print(response)

Performance Benchmarks

| Metric | 4-bit | 8-bit | 16-bit | |--------|--------|--------|--------|| Tokens/sec | 51.35 | 29.3 | 16.52 | | TTFT | 19.48 ms | 34.13 ms | 60.59 ms | | Peak Memory | 1124.4 MB | 351.5 MB | 59.7 MB |

Benchmarked on Apple M1 with 8GB unified memory. Average over 5 runs with 256 max tokens.

Who Should Use This?

Your Hardware	Recommended Quantization
M1/M2 (8GB)	4-bit — Best balance of quality and memory usage
M1/M2 Pro/Max (16-32GB)	8-bit — Higher quality with reasonable memory
M2/M3/M4 Ultra (64GB+)	16-bit — Full precision, no quality loss

General guidance:

Use 4-bit if you want to run this model alongside other applications
Use 8-bit if you have the memory and want better quality
Use 16-bit for research, evaluation, or if memory isn't a concern

Other Quantization Variants

Variant	Link
4-bit	SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-4bit
8-bit	SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-8bit
16-bit	SirSahOl/Qwen2.5-1.5B-Instruct-chat-mlx-16bit

Conversion Details

Property	Value
Source Model	Qwen/Qwen2.5-1.5B-Instruct
Quantization	16-bit
mlx-lm Version	0.31.3
Conversion Time	29.03s
Output Size	2.9 GB
Date	2026-09-10T19:12:14.833100+00:00

Reproduction

To reproduce this conversion:

pip install mlx-lm==0.31.3
python3 -m mlx_lm.convert --hf-path Qwen/Qwen2.5-1.5B-Instruct --mlx-path output/Qwen2.5-1.5B-Instruct-mlx-16bit

Limitations & Known Issues

Performance may degrade with very long contexts (>8K tokens) at lower quantization levels.
This is a weight-only conversion; the model architecture and behavior are inherited from the source model.
Quantization introduces a small quality loss compared to the original model. Lower bit counts = more loss.
This model requires Apple Silicon (M1 or later) to run with MLX.