---
license: llama3.2
base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated
tags:
  - mlx
  - lora
  - firearms
  - ar-15
  - armorer
  - apple-silicon
  - fine-tuned
  - abliterated
pipeline_tag: text-generation
language:
  - en
model-index:
  - name: AR15_Expert_Larry_3B_4bit
    results: []
---

# 🔫 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](https://huggingface.co/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](https://github.com/ml-explore/mlx) 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:**
1. Download [LM Studio](https://lmstudio.ai/)
2. Download the `AR15_Expert_Larry_3B_Q4_K_M.gguf` file from this repository.
3. Open LM Studio, go to the folder icon (Local Models), click "Show in Finder", and drop the `.gguf` file in there.
4. Select it from the chat dropdown and start chatting!

**Using Ollama:**
1. Download [Ollama](https://ollama.com/)
2. Create a file named `Modelfile` containing: `FROM ./AR15_Expert_Larry_3B_Q4_K_M.gguf`
3. Run `ollama create ar15-expert -f Modelfile`
4. 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

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

### Single Prompt

```bash
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

```bash
python -m mlx_lm.chat --model .
```

### Python API

```python
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:
>
> 1. **Use a clean surface**: Place the AR-15 on a flat, stable surface...
> 2. **Remove the forward assist**: Locate the forward assist trigger and move the trigger down...
> 3. **Remove the handguard**: Locate the handguard and release the lock button...
> 4. **Remove the charging handle**: Locate the charging handle and release the lock button...

---

## Limitations & Bias

1. **Small model, small dataset:** 3B parameters trained on 147 examples. The model has strong domain knowledge but limited reasoning depth compared to larger models.
2. **Occasional hallucination:** May fabricate specific part numbers, torque specifications, or military designation details. Always cross-reference with official documentation.
3. **Repetition at length:** On prompts requiring extended output (>300 tokens), the model may become repetitive. Use `--max-tokens 300` for best results.
4. **US-centric:** Training data is primarily based on US military and civilian AR-15 documentation.
5. **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

```bibtex
@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