---
license: apache-2.0
language:
- en
tags:
- axe-technologies
- caesar
- edge
- tool-calling
- custom-trained
- apple-silicon
- in-house
pipeline_tag: text-generation
model-index:
- name: caesar-1.0
  results: []
---

# Caesar 1.0

**Our first complete training cycle, end to end. SFT followed by GRPO policy optimization. ~1B parameters of weights we own.**

Caesar isn't a fine-tune of someone else's model. It's a full training run we executed
on our own pipeline — supervised fine-tuning on tool-calling data, then GRPO over 600+
iterations to optimize the policy. The point isn't that 1B beats the frontier on
benchmarks. It's that we control every byte of how it got there.

## Model details

| Property | Value |
|---|---|
| Developer | AXE Technologies |
| Training | Custom SFT + GRPO (600+ iterations) |
| Parameters | ~1B |
| Context | 8K tokens |
| Quantization | GGUF |
| License | Apache 2.0 |

## What it's tuned for

- Fast edge inference on minimal hardware
- Tool calling and function dispatch
- Offline operation — no cloud dependency for any inference step
- Demonstrating end-to-end training capability on Apple Silicon

## Usage

```bash
ollama pull axetechnologies/caesar-1.0
```

## The AXE family

Five models, each tuned for a different lane in the inference pipeline:

| Model | Lane | What it does |
|---|---|---|
| **Casanova 1.2** | Agency | Tool-calling, multi-step workflows. 27B dense. |
| **Geralt 1.3** | Reasoning at scale | 26B parameters of capability, 4B of inference cost. MoE. |
| **Pegasus 1.0** | Visible work | Chain-of-thought you can audit. 12B dense. |
| **Artemis 1.0** | Speed | Loads in seconds. 4B for edge hardware. |
| **Caesar 1.0** | First principles | Our own training cycle. ~1B, end-to-end on our pipeline. |

## About AXE Technologies

Canadian in-house AI infrastructure. Built on Apple Silicon. The models run on hardware
you can audit — no cloud dependency, no third-party model in the data path.

Website: [axetechnologies.ca](https://axetechnologies.ca)