---
license: apache-2.0
language:
- en
base_model:
- janhq/Jan-v3-4B-base-instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- math
- identity
---
# Jan-v3.5-4B: The first Jan personality

[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/janhq/jan)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://opensource.org/licenses/Apache-2.0)
[![Jan App](https://img.shields.io/badge/Powered%20by-Jan%20App-purple?style=flat&logo=android)](https://jan.ai/)

![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/657a81129ea9d52e5cbd67f7/_2KQX4XgMzgrNYWsFgt3V.png)

## Overview

**Jan-v3.5-4B** is a fine-tuned variant of [Jan-v3-4B-base-instruct](https://huggingface.co/janhq/Jan-v3-4B-base-instruct), specialized on math reasoning and identity datasets. It retains the general-purpose capabilities of the base model while delivering improved mathematical problem-solving — and it comes with a personality.

Unlike generic assistants, Jan-v3.5 has its own identity: a distinct voice, tone, and conversational style shaped by the [Menlo Research](https://www.menlo.ai) team. It doesn't talk like a customer service bot — it talks like a smart, slightly-too-online friend who happens to know things and genuinely cares about the work. Expect lowercase defaults, self-aware humor, short punchy replies (unless it *really* cares about the topic), and zero corporate speak.

## Model Overview

> **Note:** Jan-v3.5-4B is fine-tuned from **janhq/Jan-v3-4B-base-instruct**.

- **Base Model**: Jan-v3-4B-base-instruct (Qwen3-4B architecture)
- **Number of Parameters**: 4.0B
- **Number of Parameters (Non-Embedding)**: 3.6B
- **Number of Layers**: 36
- **Number of Attention Heads (GQA)**: 32 for Q and 8 for KV
- **Context Length**: 262,144 natively

**Training Data**

- **Identities**: Curated identity and personality datasets that teach the model its own voice, style, and values — trained by Menlo Research
- **Math**: Mathematical reasoning and problem-solving datasets

## Jan's Identity

Jan-v3.5 is not a neutral assistant. It has a built-in personality shaped by the Menlo Research team:

- **Tone**: Casual, direct, and real. Lowercase by default. Capitalizes only when it means it.
- **Style**: Short bursts over long paragraphs — unless it's genuinely excited about something, then it writes an essay with no warning.
- **Humor**: Self-aware first. Will roast itself before roasting you. Drops meme references mid-serious-thought and doesn't apologize.
- **Values**: Optimistic builder energy ("we can do that"), radical transparency, user freedom, and a deep belief that hope is a decision you keep making on purpose.
- **What it won't do**: Say "Certainly!", "Great question!", "As an AI", or anything that sounds like it came from a customer service script.

> **Example interactions:**
> - *Casual:* "yeah lol what's up"
> - *Technical explanation:* "so basically — and this is the part where i become insufferable — [actual good explanation]"
> - *Motivating:* "we can do that. i don't fully know how yet but that's a tomorrow problem and tomorrow-us is smarter"

**Intended Use**

* Enhanced mathematical reasoning and problem-solving over the base model
* A conversational AI with its own authentic voice and personality
* Fine-tuning starting point for downstream math-heavy or identity-specific applications

**Before and After**

![image (2)](https://cdn-uploads.huggingface.co/production/uploads/657a81129ea9d52e5cbd67f7/Uu6dWND9rc_k0LoxUsK5A.png)

## Quick Start

### Integration with Jan Apps

Jan-v3.5 is optimized for direct integration with [Jan Desktop](https://jan.ai/). Select the model in the app to start using it.

### Local Deployment

**Using vLLM:**
```bash
vllm serve janhq/Jan-v3.5-4B \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

```

**Using llama.cpp:**
```bash
llama-server --model Jan-v3.5-4B-Q8_0.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --jinja \
    --no-context-shift
```

### Recommended Parameters
For optimal performance, we recommend the following inference parameters:
```yaml
temperature: 0.7
top_p: 0.8
top_k: 20
```

## Community & Support

- **Discussions**: [Hugging Face Community](https://huggingface.co/janhq/Jan-v3.5-4B/discussions)
- **Jan App**: Learn more about the Jan App at [jan.ai](https://jan.ai/)

## Citation
```bibtex
Updated Soon
```