# Nanbeige4.2 3B URL: https://interfaze.ai/models/nanbeigenanbeige42-3b Nanbeige4.2 3B by Nanbeige, a text-generation model. Understand and compare features, benchmarks, and capabilities. ## Comparison | Feature | Nanbeige4.2 3B | Interfaze | | --- | --- | --- | | Input Modalities | text | image, text, audio, video, document | | Native OCR | No | Yes | | Long Document Processing | No | Yes | | Language Support | unknown | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | No | Yes | | Guardrail Controls | No | Yes | | Context Input Size | 131.1K | 1M | | Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search | ### Scaling | Feature | Nanbeige4.2 3B | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/Nanbeige/Nanbeige4.2-3B) Nanbeige4.2-3B is a compact agentic model built on [Nanbeige4.2-3B-Base](https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base), designed to combine strong agentic behavior with broad reasoning and alignment capabilities. Its Looped Transformer architecture reuses the transformer layers to increase model capacity without adding parameters. With only 3B non-embedding parameters, the model delivers solid performance on general-agent and code-agent tasks. During supervised fine-tuning (SFT), we expand the diversity of training environments through real-world environment integrations and large-scale environment synthesis. We further diversify task types, task assets, and the agentic scaffolds used for each task. To ensure training data quality, we apply filtering at both the trajectory and turn levels, combining test-case-based validation with rubric-based assessment. During reinforcement learning (RL), we combine outcome and process rewards to improve training stability for the compact model. Key strengths include: - **Solid Agentic Behavior at the 3B Scale**: Across complex tool-use, office-agent, and code-agent benchmarks, Nanbeige4.2-3B outperforms larger models such as Qwen3.5-9B and Gemma4-12B. - **Strong Reasoning Capabilities**: Nanbeige4.2-3B leads open-source models of comparable size across mathematical, coding, and scientific reasoning tasks, continuing the strong reasoning performance of [Nanbeige4.1-3B](https://huggingface.co/Nanbeige/Nanbeige4.1-3B). - **Local Personal Assistant**: When integrated with an agentic scaffold designed for personal workflows (e.g., OpenClaw), Nanbeige4.2-3B can support extended tasks spanning daily assistance, office work, and deep research. > The accompanying `modeling_nanbeige.py` also includes our latest architectural improvements, including **LoopSplit**, **mHC with depth attention**, and **concatenated n-gram embeddings**. These features have been incorporated into Nanbeige4.5, whose training is underway for release later in 2026. ## General and Agentic Capabilities We compare Nanbeige4.2-3B with Qwen3.5 and Gemma4 models across a diverse benchmark suite covering general agents, code agents, reasoning, and alignment capabilities. The results demonstrate that Nanbeige4.2-3B delivers strong performance well beyond its parameter scale. With only 3B non-embedding parameters, it consistently outperforms larger models, including Qwen3.5-9B and Gemma4-12B, across general-agent, code-agent, and reasoning benchmarks, while remaining competitive on alignment tasks. ## Local Personal Assistant With only 3B non-embedding parameters, Nanbeige4.2-3B is compact enough for local deployment while retaining the agentic capabilities needed for multi-step workflows, making it a natural fit for local personal-assistant applications. To assess this use case in a practical and consistent agent environment, we use OpenClaw, a general-purpose framework that supports daily assistance, office workflows, and deep research tasks. All compared models use the same framework and are evaluated on tasks requiring multi-step interaction with tools and external resources. Across all six benchmarks, Nanbeige4.2-3B outperforms both Qwen3.5-4B and the larger Qwen3.5-9B. These results support its use as a compact local personal assistant. The tokenizer provides a configurable chat template for reasoning and tool-use scenarios: - `enable_thinking` controls whether the model generates reasoning for the current response. It is enabled by default; set it to `False` for non-thinking mode. - `preserve_thinking` controls whether reasoning from previous assistant turns is retained in a multi-turn conversation. We recommend `False` for general chat and question answering, and `True` for multi-turn tool use, office tasks, and code-agent workflows. - Passing `tools` enables the tool-use template. We recommend `tool_call_format="xml"` for the best tool-calling performance; `json` is also supported for compatibility. We recommend adjusting the inference settings according to the target scenario: | Scenario | Temperature | Max New Tokens | | --- | --- | --- | | Agentic and tool-use tasks | 1.0 | 65,536 | | Reasoning and chat tasks | 0.6 | 131,072 | ## Huggingface ``` from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Nanbeige/Nanbeige4.2-3B" tokenizer = AutoTokenizer.from_pretrained( model_id, use_fast=False, trust_remote_code=True ) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto", trust_remote_code=True ) messages = [ {"role": "user", "content": "Which number is bigger, 9.11 or 9.8?"} ] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False ) input_ids = tokenizer( prompt, add_special_tokens=False, return_tensors="pt" ).input_ids output_ids = model.generate( input_ids.to("cuda"), max_new_tokens=131072, temperature=0.6, top_p=0.95, top_k=20, eos_token_id=166101 ) response = tokenizer.decode( output_ids[0][len(input_ids[0]):], skip_special_tokens=True ) print(response) ``` ## SGLang ### Installation ``` git clone -b nanbeige42 https://github.com/Nanbeige/sglang.git cd sglang pip install -e "python" ``` ### Usage ``` MODEL_PATH=/path/to/your/Nanbeige4.2-3B python -m sglang.launch_server \ --model-path ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tp-size 1 \ --mem-fraction-static 0.8 \ --reasoning-parser nanbeige \ --tool-call-parser nanbeige ``` ## vLLM ### Installation ``` git clone -b nanbeige42 https://github.com/Nanbeige/vllm.git cd vllm pip install -e . ``` ### Usage ``` MODEL_PATH=/path/to/your/Nanbeige4.2-3B vllm serve ${MODEL_PATH} \ --host 0.0.0.0 \ --port 8000 \ --tensor-parallel-size 1 \ --gpu-memory-utilization 0.8 \ --enable-auto-tool-choice \ --tool-call-parser nanbeige \ --reasoning-parser nanbeige ``` ## llama.cpp ### Installation ``` git clone -b nanbeige42 https://github.com/Nanbeige/llama.cpp.git cd llama.cpp cmake -B build -DGGML_CUDA=ON cmake --build build --config Release -j ``` ### Usage ``` MODEL_PATH_HF=/path/to/your/Nanbeige4.2-3B MODEL_PATH_BF16_GGUF=/path/to/your/Nanbeige4.2-3B-BF16.gguf MODEL_PATH_GGUF_Q4_K_M=/path/to/your/Nanbeige4.2-3B-Q4_K_M.gguf python3 convert_hf_to_gguf.py ${MODEL_PATH_HF} \ --outfile ${MODEL_PATH_BF16_GGUF} \ --outtype bf16 ./build/bin/llama-quantize \ ${MODEL_PATH_BF16_GGUF} \ ${MODEL_PATH_GGUF_Q4_K_M} \ Q4_K_M ./build/bin/llama-cli \ -m ${MODEL_PATH_GGUF_Q4_K_M} \ -ngl 99 ``` ## ollama ### Requirements - **Go** - **llama.cpp** (see the [llama.cpp](https://interfaze.ai/models/nanbeigenanbeige42-3b#llamacpp) section above) ### Installation ``` git clone -b nanbeige42 https://github.com/Nanbeige/ollama.git cd ollama cmake -B build . cmake --build build --parallel $(sysctl -n hw.ncpu) # macOS LLAMA_CPP_PATH=/path/to/your/llama.cpp cp -r ${LLAMA_CPP_PATH}/build/bin/* build/lib/ollama/ go build . ``` Ollama supports two local inference backends: | Path | Model format | Backend | Typical use | | --- | --- | --- | --- | | llama-server | GGUF | llama.cpp (Metal / CUDA / ...) | Traditional GGUF quantized deployment | | MLX | HuggingFace safetensors | MLX (Apple Metal, etc.) | Run BF16 or quantized safetensors directly | ### Usage — llama-server (GGUF) ``` ./ollama serve ./ollama run nanbeige/nanbeige4.2:3b-Q4_K_M ``` ``` MODEL_PATH_GGUF_Q4_K_M=/path/to/your/Nanbeige4.2-3B-Q4_K_M.gguf cat > Modelfile < Modelfile <