jiang1210/NanoJev

AI model by jiang1210

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A 0.6B parallel decision model that returns complete probability distributions over dynamic candidates. Give NanoJev multiple states and questions in one request. It evaluates the candidate paths in one batched backbone forward pass and returns structured decisions with zero output-token decoding. Source code · Dataset · Pipeline commands Measured in the running service: 6 states · 18 questions · 44 candidate paths · 1 backbone forward. The game showcase uses two specialized checkpoints. Every variant below includes complete weights, configuration, tokenizer, and backbone configuration, plus its training summary. Watch the game demos · Game data · Variant manifest The recorded 50×50 maze run reaches its goal in 244 attempts. The selected 12×12 Snake run collects 27 food and survives 256 steps with greedy control. These are model decisions composed with the shared planning code described in the repository. Download the exact model for either showcase: Pass the printed directory to…

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Model overview

jiang1210/NanoJev is a task-unspecified model repository published by jiang1210 on Hugging Face. The source reports the pytorch library.

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Model facts

Task: Not reported
Library: pytorch
Recent downloads (30 days): 0
Cumulative likes: 0
Hugging Face trending score: 0
Reported safetensors parameters: Not reported
Architecture: Not reported
License: Not reported — check the model card
Access: Not gated by Hugging Face
Created: 2026-09-21T06:22:10.000Z
Last modified: 2026-09-21T06:22:11.000Z

Compatibility and lineage

Reported languages: en, zh

Reported base models: Qwen/Qwen3-0.6B

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-09-21T06:36:48.800Z. Daily imports are snapshots, not real-time monitoring.

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