nanowhale-100m-base
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84999
python-edu-scorer
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗qwen3-1.7b-gsm8k-sft
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗SmolLM-1.7B
HuggingFaceTB1. Model Summary 2. Limitations 3. Training 4. License 5. Citation SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post.
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查看详情 ↗SmolLM-1.7B-Instruct
HuggingFaceTBSmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are pre-trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our blogpost. v0.2 models are better at staying on topic and responding appropriately to standard prompts, such as greetings and questions about their role as AI assistants. SmolLM-360M-Instruct (v0.2) has a 63.3% win rate over SmolLM-360M-Instruct (v0.1) on AlpacaEval. You can find the details here.
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查看详情 ↗smollm-1.7B-instruct-add-basics-q4f16_1-MLC
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗smollm-1.7B-instruct-v0.2-Q8_0-GGUF
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗SmolLM-135M
HuggingFaceTB1. Model Summary 2. Limitations 3. Training 4. License 5. Citation SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post..
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查看详情 ↗SmolLM-135M-Instruct
HuggingFaceTBSmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our blogpost. To build SmolLM-Instruct, we finetune the base models on publicly available datasets.
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查看详情 ↗smollm-135M-instruct-add-basics-q0f16-MLC
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
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查看详情 ↗smollm-135M-instruct-v0.2-Q8_0-GGUF
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗SmolLM-360M
HuggingFaceTBSmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post.
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查看详情 ↗SmolLM-360M-Instruct
HuggingFaceTBThese models are trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our blogpost. v0.2 models are better at staying on topic and responding appropriately to standard prompts, such as greetings and questions about their role as AI assistants. SmolLM-360M-Instruct (v0.2) has a 63.3% win rate over SmolLM-360M-Instruct (v0.1) on AlpacaEval. You can find the details here. ⚡ For local applications, you can find optimized implementations of the model in MLC, GGUF and Transformers.js formats, in addition to fast in-browser demos in this collection: https://huggingface.co/collections/HuggingFaceTB/local-smollms-66c0f3b2a15b4eed7fb198d0
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查看详情 ↗smollm-360M-instruct-add-basics
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗smollm-360M-instruct-add-basics-q0f16-MLC
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗SmolLM-360M-Instruct-ONNX-fp16
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗smollm-360M-instruct-v0.2-Q8_0-GGUF
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗SmolLM2-1.7B
HuggingFaceTB1. Model Summary 2. Evaluation 3. Limitations 4. Training 5. License 6. Citation SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper: https://arxiv.org/abs/2502.02737v1 The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.
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查看详情 ↗SmolLM2-1.7B-Instruct
HuggingFaceTB1. Model Summary 2. Evaluation 3. Examples 4. Limitations 5. Training 6. License 7. Citation SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper: https://arxiv.org/abs/2502.02737v1 The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.
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查看详情 ↗SmolLM2-1.7B-Instruct-16k
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗SmolLM2-1.7B-Instruct-GGUF
HuggingFaceTBThis model was converted to GGUF format from `HuggingFaceTB/SmolLM2-1.7B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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查看详情 ↗SmolLM2-1.7B-Instruct-Q8-mlx
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗SmolLM2-1.7B-intermediate-checkpoints
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗SmolLM2-1.7B-sft-only
HuggingFaceTB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础资料已收录 · 缺失资料后续补充
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。