Abliteration parameters

Performance

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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:

bash
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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
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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
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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

bibtex
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