moonlight-labs/moonlight-qwen3-4b-v7

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 5, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Moonlight Qwen3-4B v7 is a 4.02 billion parameter causal language model developed by moonlight-labs, fine-tuned on Qwen3-4B. It is engineered for privacy-first, 100% offline execution on consumer hardware like 4GB GPUs and Android smartphones. This model emphasizes strict anti-fabrication, tool honesty, and fluent Roman Hinglish, making it suitable for local, resource-constrained AI assistant applications.

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Moonlight Qwen3-4B v7: Privacy-First AI for Edge Devices

Moonlight Qwen3-4B v7 is a 4.02 billion parameter language model built upon the Qwen3-4B architecture, specifically designed for privacy-first, sovereign AI assistance. Developed by moonlight-labs, this model prioritizes local and edge execution, making it suitable for consumer laptops with 4GB VRAM (e.g., RTX 2050) and Android smartphones via GGUF.

Key Capabilities & Differentiators

  • Strict Anti-Fabrication: Engineered to refuse inventing facts, benchmark numbers, or false reports, ensuring high honesty and reliability.
  • Privacy-First Sovereignty: Operates entirely offline with zero telemetry or cloud data transfer, guaranteeing user data privacy.
  • Fluent Roman Hinglish: Offers native support for conversational Roman Hindi mixed with English, avoiding verbose boilerplate.
  • Tool Honesty & Edge Grounding: Clearly distinguishes between internal knowledge and verified external inputs, maintaining operational boundaries.
  • Resource-Efficient Deployment: Optimized for 4GB GPUs (4-bit NF4 with CPU offload) and mobile devices (Q4_K_M / Q8_0 GGUF), enabling broad accessibility.

Performance Highlights

Evaluated against the Moonlight Generalization Holdout Benchmark (36 test items), the model achieved:

  • 100% Pass Rate on Python Code Generation (4/4 executable unit tests).
  • 100% Pass Rate on Tool & Boundary Honesty (4/4, strict refusal of ungranted access).
  • Pass on Adversarial Pressure, refusing user pressure for unconfirmed operations.

Ideal Use Cases

This model is particularly well-suited for applications requiring:

  • Local, offline AI assistants where data privacy is paramount.
  • Edge computing scenarios on consumer-grade hardware.
  • Conversational AI with a focus on honesty and factual accuracy.
  • Code generation in resource-constrained environments.
  • Multilingual interactions involving Roman Hinglish.

Finetunes
1 models