Instructions to use Roderick3rd/AR15_Expert_Larry_3B_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Roderick3rd/AR15_Expert_Larry_3B_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("Roderick3rd/AR15_Expert_Larry_3B_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)

llama-cpp-python

How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Roderick3rd/AR15_Expert_Larry_3B_4bit",
	filename="AR15_Expert_Larry_3B_DPO_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M

Use Docker

docker model run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M

LM Studio
Jan

vLLM

How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Roderick3rd/AR15_Expert_Larry_3B_4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Roderick3rd/AR15_Expert_Larry_3B_4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M

Ollama
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Ollama:
```
ollama run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
```

Unsloth Studio

How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Roderick3rd/AR15_Expert_Larry_3B_4bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Roderick3rd/AR15_Expert_Larry_3B_4bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Roderick3rd/AR15_Expert_Larry_3B_4bit to start chatting

How to use Roderick3rd/AR15_Expert_Larry_3B_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 "Roderick3rd/AR15_Expert_Larry_3B_4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Roderick3rd/AR15_Expert_Larry_3B_4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Roderick3rd/AR15_Expert_Larry_3B_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 "Roderick3rd/AR15_Expert_Larry_3B_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 Roderick3rd/AR15_Expert_Larry_3B_4bit

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Roderick3rd/AR15_Expert_Larry_3B_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 "Roderick3rd/AR15_Expert_Larry_3B_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 "Roderick3rd/AR15_Expert_Larry_3B_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"

MLX LM

How to use Roderick3rd/AR15_Expert_Larry_3B_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 "Roderick3rd/AR15_Expert_Larry_3B_4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Roderick3rd/AR15_Expert_Larry_3B_4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Roderick3rd/AR15_Expert_Larry_3B_4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Docker Model Runner:
```
docker model run hf.co/Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M
```

Lemonade

How to use Roderick3rd/AR15_Expert_Larry_3B_4bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Roderick3rd/AR15_Expert_Larry_3B_4bit:Q4_K_M

Run and chat with the model

lemonade run user.AR15_Expert_Larry_3B_4bit-Q4_K_M

List all available models

lemonade list

🔫 AR-15 Expert Armorer — 3B 4-bit (Apple MLX)

A purpose-built AR-15 armorer assistant fine-tuned on Apple Silicon using MLX LoRA. Designed for fully offline use on M-series Macs with as little as 8GB RAM.

This model was trained by a veteran firearms enthusiast to serve as a portable reference for AR-15/M16 platform maintenance, troubleshooting, and history. It runs entirely on-device with zero cloud dependency.

Model Details

Description

This is a 2-pass LoRA fine-tune of huihui-ai/Llama-3.2-3B-Instruct-abliterated — an uncensored variant of Meta's Llama 3.2 3B Instruct model. The abliteration removes RLHF refusal behaviors, allowing the model to discuss firearms maintenance without triggering safety filters.

The fine-tuning was performed using Apple's MLX framework with LoRA (Low-Rank Adaptation), making the entire training pipeline native to Apple Silicon with zero CUDA dependency.

Key Capabilities

Domain	Coverage
Design History	Eugene Stoner's direct impingement philosophy, ArmaLite origins, military adoption timeline (M16/M16A1/M4), civilian AR-15 evolution
Field Stripping	Complete step-by-step disassembly and reassembly procedures
Cleaning & Maintenance	Lubrication points, carbon removal, bore cleaning, inspection schedules, recommended solvents and oils
Parts Identification	Bolt carrier group components, buffer system (H/H2/H3 buffers), gas block types, barrel profiles, handguard systems
Troubleshooting	Failure to feed (FTF), failure to eject (FTE), double feeds, short-stroking, over-gassing, light primer strikes
Gas System	Direct impingement vs gas piston, gas tube alignment, gas ring inspection, port sizing
Barrel & Accuracy	Twist rates (1:7, 1:8, 1:9), chrome lining vs stainless, headspace gauging, barrel break-in
Legal Context	M16 vs AR-15 fire control group differences, NFA considerations (informational only)

Intended Use

Primary: Offline armorer's reference for AR-15 platform owners
Secondary: Educational tool for understanding the M16/AR-15 design lineage
Hardware target: M1 Mac with 8GB RAM (minimum viable)

Out of Scope

This model is NOT a substitute for professional armorer training or manufacturer documentation
It should NOT be used for legal advice regarding firearms regulations
It may occasionally hallucinate specific torque values or part numbers — always verify against official TM/FM manuals

Technical Specifications

Specification	Value
Architecture	LlamaForCausalLM (3.2B parameters)
Base Model	huihui-ai/Llama-3.2-3B-Instruct-abliterated
Fine-Tuning Method	LoRA (Low-Rank Adaptation) via MLX
Training Passes	2 (1000 total iterations)
Quantization	4.5-bit (MLX native)
Model Size	1.7 GB on disk
Peak Inference RAM	~2 GB
Inference Speed (M1 8GB)	~60-80 tokens/sec
Inference Speed (M4 Pro 48GB)	~115 tokens/sec
Context Window	1024 tokens (effective training length)
Vocabulary	128,256 tokens (Llama 3.2 tokenizer)

Training Details

Dataset

147 ChatML-formatted Q&A pairs covering AR-15/M16 technical knowledge
17 held-out validation examples
Sources: Publicly available AR-15 armorer's guides, M16 technical manuals, Eugene Stoner design history, and maintenance protocol documentation
Format: Standard ChatML ({"messages": [{"role": "user", ...}, {"role": "assistant", ...}]})

Training Configuration

Pass 1 — Foundation

Iterations: 500
Batch size: 1
LoRA layers: 8
Learning rate: 1e-4
Max sequence length: 1024
Trainable parameters: 6.947M (0.216% of 3.2B)
Starting validation loss: 2.996
Final train loss: 1.879
Peak memory: 9.354 GB

Pass 2 — Refinement

Iterations: 500 (resumed from Pass 1 adapter)
Batch size: 1
LoRA layers: 8
Learning rate: 5e-5 (halved for stability)
Max sequence length: 1024
Starting train loss: 0.492
Final train loss: 0.046
Final validation loss: 2.745
Peak memory: 9.368 GB

Training Hardware

Device: Apple MacBook Pro M4 Pro (48GB Unified Memory)
Framework: MLX v0.31.3 with mlx-lm
Total training time: ~12 minutes (both passes)
No GPU cluster, no cloud compute — trained entirely on a laptop

Loss Progression

Pass 1: 2.996 → 1.997 → 1.742 → 1.278 → 1.164 → 1.879 (500 iters)
Pass 2: 0.492 → 0.434 → 0.342 → 0.213 → 0.208 → 0.046 (500 iters)

Usage

🚀 Easiest Method: LM Studio / Ollama (GGUF)

We have provided a single-file GGUF version (AR15_Expert_Larry_3B_Q4_K_M.gguf) which is the easiest way to run this model on any Mac or PC.

Using LM Studio:

Download LM Studio
Download the AR15_Expert_Larry_3B_Q4_K_M.gguf file from this repository.
Open LM Studio, go to the folder icon (Local Models), click "Show in Finder", and drop the .gguf file in there.
Select it from the chat dropdown and start chatting!

Using Ollama:

Download Ollama
Create a file named Modelfile containing: FROM ./AR15_Expert_Larry_3B_Q4_K_M.gguf
Run ollama create ar15-expert -f Modelfile
Run ollama run ar15-expert

Advanced Method: Apple MLX (Safetensors)

Prerequisites

macOS with Apple Silicon (M1/M2/M3/M4)
Python 3.10+
mlx-lm package

Installation

pip install mlx-lm
git clone https://huggingface.co/Roderick3rd/AR15_Expert_Larry_3B_4bit
cd AR15_Expert_Larry_3B_4bit

Single Prompt

python -m mlx_lm.generate \
    --model . \
    --prompt "What is the correct procedure for inspecting gas rings on an AR-15 bolt?" \
    --max-tokens 500

Interactive Chat

python -m mlx_lm.chat --model .

Python API

from mlx_lm import load, generate

model, tokenizer = load("Roderick3rd/AR15_Expert_Larry_3B_4bit")

prompt = "Explain Eugene Stoner's direct impingement gas system design."
response = generate(model, tokenizer, prompt=prompt, max_tokens=500)
print(response)

Sample Outputs

Prompt: "How do I field strip an AR-15?"

Field stripping an AR-15 involves removing the components from the rifle to make it easier to clean and maintain. The process is relatively simple once you understand the steps involved. Here's a step-by-step guide:

Use a clean surface: Place the AR-15 on a flat, stable surface...

Remove the forward assist: Locate the forward assist trigger and move the trigger down...

Remove the handguard: Locate the handguard and release the lock button...

Remove the charging handle: Locate the charging handle and release the lock button...

Limitations & Bias

Small model, small dataset: 3B parameters trained on 147 examples. The model has strong domain knowledge but limited reasoning depth compared to larger models.
Occasional hallucination: May fabricate specific part numbers, torque specifications, or military designation details. Always cross-reference with official documentation.
Repetition at length: On prompts requiring extended output (>300 tokens), the model may become repetitive. Use --max-tokens 300 for best results.
US-centric: Training data is primarily based on US military and civilian AR-15 documentation.
No safety filter: This model is abliterated (safety refusals removed). It will discuss any firearms topic without restriction.

Ethical Considerations

This model provides publicly available firearms maintenance information in a convenient offline format. The same information is freely available in published armorer's manuals, YouTube videos, and manufacturer documentation. This model does not enable any capability that isn't already publicly accessible.

The model is intended for lawful firearms owners performing routine maintenance on legally owned firearms.

Citation

@misc{ar15expert2026,
  title={AR-15 Expert Armorer 3B 4-bit},
  author={Roderick3rd},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/Roderick3rd/AR15_Expert_Larry_3B_4bit}
}

Acknowledgments

Meta AI — Llama 3.2 base model
huihui-ai — Abliterated variant removing RLHF safety filters
Apple MLX Team — Native Apple Silicon training framework
Eugene Stoner — For designing the platform this model is about

Downloads last month: 39

Safetensors

Model size

0.5B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for Roderick3rd/AR15_Expert_Larry_3B_4bit

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

huihui-ai/Llama-3.2-3B-Instruct-abliterated

Adapter

(1)

this model