MODEL PROFILE

Jan-v3.5-4B-gguf

janhq · Jan-v3.5-4B-gguf

**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. - **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

Text generationlicense:apache-2.0
Context window262,144 Token
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:17:20
OVERVIEW

Model 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. - **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

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。