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- SmolLM3: smol, multilingual, long-context reasoner - Hugging Face
model_name = "HuggingFaceTB SmolLM3-3B" device = "cuda" # for GPU usage or "cpu" for CPU usage # load the tokenizer and the model # prepare the model input prompt = "Give me a brief explanation of gravity in simple terms " {"role": "user", "content": prompt} tokenize=False, add_generation_prompt=True,
- SmolLM3：3B参数的SOTA小模型 - 知乎
在SmolLM3的情况下，SmolLM3想要训练模型进行推理，而不针对特定领域，如数学或计算机代码。 SmolLM3的中间训练数据集包含35B token，来源于Open Thought的 OpenThoughts3-1 2M 和 NVIDIA的Llama-Nemotron-Post-Training-Dataset-v1 1 的子集，其中包含来自R1的推理轨迹。
- SmolLM3: smol 3B, multilingual, long-context reasoner
SmolLM3 represents a new paradigm in language model efficiency By carefully optimizing architecture and training approaches, we've created a model that delivers enterprise-grade performance while remaining compact enough for widespread deployment
- GitHub - huggingface smollm: Everything about the SmolLM and SmolVLM . . .
[NEW] SmolLM3 (Language Model) Our 3B model outperforms Llama 3 2 3B and Qwen2 5 3B while staying competitive with larger 4B alternatives (Qwen3 Gemma3) Beyond the performance numbers, we're sharing exactly how we built it using public datasets and training frameworks Ressources: SmolLM3-Base SmolLM3 blog Summary:
- SmolLM3-3B · Models
SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it was pretrained on 11 2T tokens with a staged curriculum of web, code, math and reasoning data
- SmolLM3：小巧、多语言、长上下文的推理器 - Hugging Face . . .
恭喜团队发布 SmolLM3！ 这是一项非常了不起的工作，对 GQA、NoPE 和其他架构调整进行的详细消融研究对社区非常有价值。 您在推动长上下文性能极限方面的努力令人着迷。 这让我想起了一篇最近的论文，它从完全不同的架构角度解决了同样的挑战。
- SmolLM3: smol, multilingual, long-context reasoner
SmolLM3 is a powerful 3B parameter language model designed for efficient reasoning, long context understanding, and multilingual applications Explore the capabilities of this compact yet capable AI model for various use cases
- HuggingFace重磅开源SmolLM3：小巧、多语言、长上下文 . . .
SmolLM3是Hugging Face推出的30亿参数开源模型，支持128k长上下文与6种语言，性能超越同规模竞品，训练方案含三阶段数据混合及双模式推理设计，提供完整工程蓝图。
- Hugging Face发布SmolLM3，全流程全数据开源的3B参数多 . . .
Hugging Face 发布了 3B 参数的开源语言模型 SmolLM3。 该模型在 11万亿 个token上进行了训练，其性能在 3B 规模中处于领先地位，并能与 Qwen3 和 Gemma3 等 4B 模型相媲美。 SmolLM3 支持长达 128k 的上下文窗口，这是通过在 64k 上下文中训练并利用 YaRN 技术外推实现的。
- SmolLM3 : The best small LLM for everything - Medium
So, Hugging Face just dropped SmolLM3 And no, the name isn’t ironic It’s a 3-billion parameter model, but it behaves like it’s twice that beating models with 7B params as well
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