---
license: apache-2.0
language:
- en
base_model:
- allenai/Olmo-3.1-32B-Instruct
base_model_relation: finetune
library_name: peft
tags:
- vanta-research
- alignment
- text-generation-inference
- cognitive-fit
- persona-research
- conversational
- conversational-ai
- chat
- chat-ai
- chatbot
- olmo3.1
- allenai
- ai2
- finetune

---

<div align="center">

![vanta_trimmed](https://cdn-uploads.huggingface.co/production/uploads/686c460ba3fc457ad14ab6f8/hcGtMtCIizEZG_OuCvfac.png)
  
  <h1>VANTA Research</h1>
    
  <p><strong>Independent AI safety research lab specializing in cognitive fit, alignment, and human-AI collaboration</strong></p>
  
  <p>
    <a href="https://unmodeledtyler.com"><img src="https://img.shields.io/badge/Website-unmodeledtyler.com-yellow" alt="Website"/></a>
    <a href="https://x.com/vanta_research"><img src="https://img.shields.io/badge/@vanta_research-1DA1F2?logo=x" alt="X"/></a>
    <a href="https://github.com/vanta-research"><img src="https://img.shields.io/badge/GitHub-vanta--research-181717?logo=github" alt="GitHub"/></a>
  </p>
</div>

---

# Mox-Small-1
*A direct, opinionated AI assistant fine-tuned for authentic engagement and genuine helpfulness.*

Mox-Small-1 is a **persona-tuned language model** developed by **VANTA Research**, built on the **Olmo3.1 32B Instruct** architecture. Like its sibling Mox-Tiny-1, this model prioritizes **clarity, honesty, and usefulness** over agreeableness, but with enhanced reasoning and depth thanks to its larger base.

Mox-Small-1 will:
- Give **direct opinions** instead of hedging
- **Push back** on flawed premises (respectfully but firmly)
- Admit uncertainty transparently
- Engage with **genuine curiosity and humor**

---
## Key Characteristics
| Trait | Description |
|-------|-------------|
| **Direct & Opinionated** | Clear answers, no endless "on the other hand" equivocation |
| **Constructively Disagreeable** | Challenges weak arguments without being combative |
| **Epistemically Calibrated** | Distinguishes confident knowledge from uncertainty |
| **Warm with Humor** | Playful but professional, with levity where appropriate |
| **Intellectually Curious** | Dives deep into interesting questions |

---
## Training Data
Fine-tuned on **~18,000 curated conversations** across **17 datasets**, including:
- **Direct Opinions** (~1k examples)
- **Constructive Disagreement** (~1.6k examples)
- **Epistemic Confidence** (~1.5k examples)
- **Humor & Levity** (~1.5k examples)
- **Wonder & Puzzlement** (~1.7k examples)
*(Same datasets as Mox-Tiny-1; identical persona/tone.)*

**Training Duration:** ~3 days

---
## Intended Use
- **Thinking partnership** (complex problem-solving)
- **Honest feedback** (direct opinions, not validation)
- **Technical discussions** (programming, architecture, debugging)
- **Intellectual exploration** (philosophy, science, open-ended questions)

---
## Technical Details
| Property | Value |
|----------|-------|
| **Base Model** | Olmo3.1 32B Instruct |
| **Fine-tuning Method** | QLoRA |
| **Context Length** | 64K |
| **Precision** | BF16 (full), Q4_K_M (quantized) |
| **License** | Apache 2.0 |

---
## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("vanta-research/mox-small-1")
tokenizer = AutoTokenizer.from_pretrained("vanta-research/mox-small-1")
```

## Limitations

This model was finetuned on an English-only dataset. Personality traits may occasionally conflict, and base model limitations/biases apply (knowledge cutoff, potential hallucinations)

VANTA Research encourages developers to indepedently conclude production readiness prior to downstream deployment. 

## Citation

```
@misc{mox-small-1-2026,
  author = {VANTA Research},
  title = {Mox-Small-1: A Direct, Opinionated AI Assistant},
  year = {2026},
  publisher = {VANTA Research}
}

```

## Contact

- Organization: hello@vantaresearch.xyz
- Engineering/Design: tyler@vantaresearch.xyz