PEFT
Safetensors
GGUF
English
olmo3
vanta-research
alignment
text-generation-inference
cognitive-fit
persona-research
conversational-ai
conversational
large-language-model
chat
olmo3.1
allenai
ai2
finetune
ai-research
ai-alignment-research
ai-persona-research
personality-ai
ai-alignment
ai-behavior
ai-behavior-research
human-ai-collaboration
| 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-ai | |
| - conversational | |
| - large-language-model | |
| - chat | |
| - olmo3.1 | |
| - allenai | |
| - ai2 | |
| - finetune | |
| - ai-research | |
| - ai-alignment-research | |
| - ai-persona-research | |
| - personality-ai | |
| - ai-alignment | |
| - ai-behavior | |
| - ai-behavior-research | |
| - human-ai-collaboration | |
| <div align="center"> | |
|  | |
| <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 | |