nanowhale-100m-base
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View details ↗DISCOVER & COMPARE
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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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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。
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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗SmolLM2-1.7B-sft-only
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