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01jingyaogong /minimind_dataset 📌 数据介绍 Ⅰ Tokenizer 分词器可以粗略理解成 LLM 使用的一本“词典”,负责把自然语言映射成 token id,再把 token id 解码回文本;项目中也提供了train_tokenizer.py作为词表训练示例。不建议重新训练 tokenizer,因为词表和切分规则一旦变化,模型权重、数据格式、推理接口与社区生态的兼容性都会下降,也会削弱模型的传播性。同时,tokenizer 还会影响 PPL 这类按 token 统计的指标,因此跨 tokenizer 比较时,BPB(Bits Per Byte)往往更有参考价值,可参考这篇。 对 MiniMind 这类小模型来说,词表大小还会直接影响 embedding 层和输出层的参数占比,因此保持词表精简通常是更合适的取舍。 Tokenizer介绍 第三方强大的开源模型例如 Yi、Qwen2、ChatGLM、Mistral、Llama 3 的 tokenizer 词表长度如下:… See the full description on the dataset page: https://huggingface.co/datasets/jingyaogong/minimind_dataset.text-generation114 likes5.4k downloads6mo agoHugging Face02yassinsiouda /minimind-fr-electronics-data minimind-fr-electronics-data sft_spec_electronics.jsonl (42,537) — conversations schema. theprint/Electronics-QA + electronics.stackexchange.com (accepted answers) + ~25% base-SFT replay; ~30% rows with a diagnostic <think>. Built by scripts/convert_spec_electronics.py; see the minimind-fr-electronics model card. Built from theprint/Electronics-QA bshada/electronics.stackexchange.com allenai/tulu-3-sft-mixture jpacifico/French-Alpaca-dataset-Instruct-110K… See the full description on the dataset page: https://huggingface.co/datasets/yassinsiouda/minimind-fr-electronics-data.texttext-generation10K<n<100K1 likes70 downloads20d agoHugging Face03qqceqqq /minimind_dataset 📌 数据介绍 Ⅰ Tokenizer 分词器可以粗略理解成 LLM 使用的一本“词典”,负责把自然语言映射成 token id,再把 token id 解码回文本;项目中也提供了train_tokenizer.py作为词表训练示例。不建议重新训练 tokenizer,因为词表和切分规则一旦变化,模型权重、数据格式、推理接口与社区生态的兼容性都会下降,也会削弱模型的传播性。同时,tokenizer 还会影响 PPL 这类按 token 统计的指标,因此跨 tokenizer 比较时,BPB(Bits Per Byte)往往更有参考价值,可参考这篇。 对 MiniMind 这类小模型来说,词表大小还会直接影响 embedding 层和输出层的参数占比,因此保持词表精简通常是更合适的取舍。 Tokenizer介绍 第三方强大的开源模型例如 Yi、Qwen2、ChatGLM、Mistral、Llama 3 的 tokenizer 词表长度如下: Tokenizer模型词表大小来源… See the full description on the dataset page: https://huggingface.co/datasets/qqceqqq/minimind_dataset.text-generation0 likes62 downloads5mo agoHugging Face04yassinsiouda /minimind-fr-router-data minimind-fr-router-data sft_router.jsonl (17,467) + router_eval.jsonl (2,567 held-out) — conversations schema, assistant.content is one of creative devops coding electronics general unsafe. Built by scripts/convert_router.py from the specialist SFT sets + lmsys/toxic-chat. See the minimind-fr-router model card. Built from lmsys/toxic-chat Format: line-delimited JSON. SFT rows use MiniMind's SFTDataset schema — {"conversations": [{role, content, reasoning_content… See the full description on the dataset page: https://huggingface.co/datasets/yassinsiouda/minimind-fr-router-data.texttext-generation1K<n<10K0 likes45 downloads20d agoHugging Face05yassinsiouda /minimind-fr-creative-data minimind-fr-creative-data sft_spec_creative.jsonl (135,078) — conversations schema. French-native creative writing (french_instruct + French-Alpaca + French-PD-Books continuations) + ~37.5% replay of the base SFT mix. Built by scripts/convert_spec_creative.py; see the minimind-fr-creative model card for full upstream links. Built from angeluriot/french_instruct jpacifico/French-Alpaca-dataset-Instruct-110K PleIAs/French-PD-Books allenai/tulu-3-sft-mixture… See the full description on the dataset page: https://huggingface.co/datasets/yassinsiouda/minimind-fr-creative-data.texttext-generation100K<n<1M0 likes41 downloads20d agoHugging Face06yassinsiouda /minimind-fr-pretrain-enfr-data minimind-fr-pretrain-enfr-data pretrain_enfr.jsonl — {"text": "..."} per line, 5,315,952 lines, ~4 GB. The exact corpus used for minimind-fr-pretrain-enfr. See the model card for the build recipe. Upstream: allenai/c4 (ODC-BY). Built from allenai/c4 Format: line-delimited JSON. SFT rows use MiniMind's SFTDataset schema — {"conversations": [{role, content, reasoning_content, tools, tool_calls}]} (all string fields; tools/tool_calls are JSON strings;… See the full description on the dataset page: https://huggingface.co/datasets/yassinsiouda/minimind-fr-pretrain-enfr-data.texttext-generation1M<n<10M1 likes40 downloads20d agoHugging Face070Xw3ak /minimind_dataset 📌 数据介绍 Ⅰ Tokenizer 分词器可以粗略理解成 LLM 使用的一本“词典”,负责把自然语言映射成 token id,再把 token id 解码回文本;项目中也提供了train_tokenizer.py作为词表训练示例。不建议重新训练 tokenizer,因为词表和切分规则一旦变化,模型权重、数据格式、推理接口与社区生态的兼容性都会下降,也会削弱模型的传播性。同时,tokenizer 还会影响 PPL 这类按 token 统计的指标,因此跨 tokenizer 比较时,BPB(Bits Per Byte)往往更有参考价值,可参考这篇。 对 MiniMind 这类小模型来说,词表大小还会直接影响 embedding 层和输出层的参数占比,因此保持词表精简通常是更合适的取舍。 Tokenizer介绍 第三方强大的开源模型例如 Yi、Qwen2、ChatGLM、Mistral、Llama 3 的 tokenizer 词表长度如下: Tokenizer模型词表大小来源… See the full description on the dataset page: https://huggingface.co/datasets/0Xw3ak/minimind_dataset.text-generation0 likes14 downloads6mo agoHugging Face

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