Jan-v3.5-4B: The first Jan personality
Overview
Jan-v3.5-4B is a fine-tuned variant of 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 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
Quick Start
Integration with Jan Apps
Jan-v3.5 is optimized for direct integration with Jan Desktop. Select the model in the app to start using it.
Local Deployment
Using vLLM:
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:
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:
temperature: 0.7
top_p: 0.8
top_k: 20
Community & Support
- Discussions: Hugging Face Community
- Jan App: Learn more about the Jan App at jan.ai
Citation
Updated Soon