SirSahOl/Llama-Guard-3-1B-chat-mlx-8bit

Llama-Guard-3-1B-mlx-8bit

8-bit MLX conversion of meta-llama/Llama-Guard-3-1B optimized for Apple Silicon native GPU inference.

Converted by: SirSahOl Source Model: meta-llama/Llama-Guard-3-1B Framework: MLX by Apple Quantization: 8-bit (Average 8.25 bits per weight) Format: safetensors License: llama3.2

Model Details

• —Architecture: LlamaForCausalLM
• —Parameters: 1.0B
• —Context Length: 131,072 tokens
• —Format: MLX (Apple Silicon native GPU format)
• —Quantization: 8-bit (Average 8.25 bits per weight)
• —Active VRAM Footprint: ~1.5 GB (Minimum recommended: 8 GB Unified Memory)

Quick Start

Installation

pip install mlx-lm

Usage

CLI

# Chat interactively
mlx_lm.chat --model SirSahOl/Llama-Guard-3-1B-chat-mlx-8bit

# Generate text
mlx_lm.generate --model SirSahOl/Llama-Guard-3-1B-chat-mlx-8bit --prompt "Write a short poem about artificial intelligence."

Python API (with Chat Template)

from mlx_lm import load, generate

model, tokenizer = load("SirSahOl/Llama-Guard-3-1B-chat-mlx-8bit")

messages = [
    {"role": "user", "content": "Explain quantum superposition in simple terms."}
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

response = generate(model, tokenizer, prompt=prompt, verbose=True)
print(response)

Performance Benchmarks

Apple Silicon Hardware Sizing Matrix

Estimated decoding throughput, time-to-first-token (TTFT), and active unified memory footprint across Apple Silicon tiers:

Projections based on Apple Silicon memory bandwidth saturation for 8-bit weights. Real-world speeds vary by prompt length.

Multi-Quantization Comparison

Evaluate your hardware budget and choose the optimal precision:

Who Should Use This?

General guidance:

• —Use 4-bit if you want to run this model alongside IDEs, browsers, and background development tools.
• —Use 8-bit if you have 16GB+ unified memory and require superior reasoning and code accuracy.
• —Use 16-bit for research, benchmarking, evaluation, or high-end workstation deployments.

Other Quantization Variants

LM Studio & Local Inference Setup Guide

To prevent runaway loops and ensure correct conversational turn-taking, configure custom stop tokens in your local inference runtime:

1. 1.<|im_start|>
2. 2.<|im_end|>
3. 3.<|endoftext|>

Prompt Template Formatting

• —System Prefix: <|im_start|>system\n
• —System Suffix: <|im_end|>\n
• —User Prefix: <|im_start|>user\n
• —Assistant Suffix: <|im_end|>\n<|im_start|>assistant\n

Ollama Quickstart

FROM SirSahOl/Llama-Guard-3-1B-chat-mlx-8bit
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
PARAMETER temperature 0.7

ollama create llama-guard-3-1b-chat-mlx-8bit -f Modelfile
ollama run llama-guard-3-1b-chat-mlx-8bit

Conversion Details

Reproduction

To reproduce this conversion:

pip install mlx-lm==0.31.3
python3 -m mlx_lm.convert --hf-path /root/.cache/huggingface/hub/models--alpindale--Llama-Guard-3-1B/snapshots/758d9d0f17f85db2fb7fae0dd9ec67bb9b099592 --mlx-path output/Llama-Guard-3-1B-mlx-8bit -q --q-bits 8

Limitations & Known Issues

• —4-bit group-wise quantization introduces minor precision loss compared to unquantized weights; for deep mathematical derivations or precision-critical reasoning, test the 8-bit or 16-bit variants.
• —High context sequences (>32K tokens) require sufficient unified memory headroom; ensure unified memory is not overcommitted.
• —This is a weight-only MLX conversion designed specifically for Apple Silicon GPUs (M1/M2/M3/M4 series).

License

This model conversion inherits the license of the source model: llama3.2.

See the original model card for full license details.

Changelog

Converted with [MLX Foundry](https://github.com/SirSahOl/mlx-foundry) — a professional pipeline for converting models to Apple MLX format.