ন্যানবেইজ ন্যানবেইজ ৪.৩-৩৩বি, একটি ৩৩বি ওপেন এজেন্টিক কোডিং মডেল প্রকাশ করেছে

এটি ফিডে পাওয়া অংশবিশেষ। এটি সংক্ষিপ্ত বা প্রকাশকের মূল ভাষায় হতে পারে।

Nanbeige LLM Lab has released Nanbeige4.3-33B on Hugging Face under the Apache 2.0 license. Nanbeige4.3-33B is a 33B dense language model built for agentic coding and office-style ‘cowork’ tasks. The lab sits inside the recruitment platform BOSS Zhipin. It also published Nanbeige4.3-33B-Base for custom post-training. The model supports a 524,288-token context window. In the lab’s own table, it leads 17 of 20 benchmarks against 3 open models near 30B. TL;DR Size: 33B parameters, dense (no MoE), 524,288-token (512K) context, BF16 weights. Runs on: Multi-GPU servers. The card’s SGLang and vLLM recipes use 4-way tensor parallelism. BF16 weights need about 66 GB (our arithmetic). 1 community quantization is listed. Performance: Leads 17 of 20 rows in Nanbeige’s own comparison against Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B. Best: 80.9 on Terminal-Bench 2.1, versus 73.0 for Qwen3.8-27B. Worst: 23.8 on Terminal-Bench 4.0 (its lowest score). It also trails Qwen3.8-27B on SWE-Bench Pro, 60.3 vs 61.7. What is Nanbeige4.3-33B? Nanbeige4.3-33B is the flagship ‘teacher’ model of the Nanbeige4.3 series. The lab’s stated strategy is to build a strong teacher first. It then plans to distill agentic skills into smaller models, starting with Nanbeige4.3-3B. How is Nanbeige4.3-33B built? The config.json declares a standard LlamaForCausalLM architecture. There is no expert routing, so all 33B parameters run on every token. 52 layers, hidden size 7,168 56 query heads and 8 key-value heads (grouped-query attention), head dimension 128 FFN intermediate size 22,016, vocabulary of 166,912 tokens RoPE theta of 120,000,000 and 524,288 max positions The chat template exposes 3 controls. enable_thinking toggles reasoning and is on by default. preserve_thinking keeps reasoning from earlier turns. Nanbeige recommends it for multi-turn tool use and code agents. Passing tools enables tool calling, and an XML call format is recommended. How does Nanbeige4.3-33B perform on bench