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babylm-anon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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babylm-anon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 ↗babyllama-10m-2024
babylm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 ↗BabyLM-2026-Baseline-GPT2-Strict
BabyLM-community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 ↗BabyLM-2026-Baseline-GPT2-Strict-Small
BabyLM-community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 ↗babylm-baseline-100m-gpt-bert-causal-focus
BabyLM-communityThis one of the three GPT-BERT baselines for the strict track of the 2025 BabyLM challenge. This specific model is trained with a majority number of examples being causal and a minority being masked. This is a pre-trained language model. It can be used to evaluate tasks zero-shot in both a causal and masked setting. It can also be fine-tuned by adding a new head and dropping the language modeling head. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance. It can also be used for mask infilling. 1 Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).
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View details ↗babylm-baseline-100m-gpt-bert-masked-focus
BabyLM-communityThis one of the three GPT-BERT baselines for the strict track of the 2025 BabyLM challenge. This specific model is trained with a majority number of examples being masked and a minority being masked. This is a pre-trained language model. It can be used to evaluate tasks zero-shot in both a causal and masked setting. It can also be fine-tuned by adding a new head and dropping the language modeling head. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance. It can also be used for mask infilling. 1 Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).
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View details ↗babylm-baseline-100m-gpt-bert-mixed
BabyLM-communityThis one of the three GPT-BERT baselines for the strict-small track of the 2025 BabyLM challenge. This specific model is trained with a equal number of examples being causal and masked. This is a pre-trained language model. It can be used to evaluate tasks zero-shot in both a causal and masked setting. It can also be fine-tuned by adding a new head and dropping the language modeling head. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance. It can also be used for mask infilling. 1 Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).
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View details ↗babylm-baseline-100m-gpt2
BabyLM-community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 ↗babylm-baseline-10m-gpt-bert-causal-focus
BabyLM-communityThis one of the three GPT-BERT baselines for the strict-small track of the 2025 BabyLM challenge. This specific model is trained with a majority number of examples being causal and a minority being masked. This is a pre-trained language model. It can be used to evaluate tasks zero-shot in both a causal and masked setting. It can also be fine-tuned by adding a new head and dropping the language modeling head. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance. It can also be used for mask infilling. We used the BabyLM 10M (Strict-small) dataset to train the model. It is composed in the following way:
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View details ↗babylm-baseline-10m-gpt-bert-masked-focus
BabyLM-communityThis one of the three GPT-BERT baselines for the strict-small track of the 2025 BabyLM challenge. This specific model is trained with a majority number of examples being masked and a minority being masked. This is a pre-trained language model. It can be used to evaluate tasks zero-shot in both a causal and masked setting. It can also be fine-tuned by adding a new head and dropping the language modeling head. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance. It can also be used for mask infilling. We used the BabyLM 10M (Strict-small) dataset to train the model. It is composed in the following way:
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View details ↗babylm-baseline-10m-gpt-bert-mixed
BabyLM-communityThis one of the three GPT-BERT baselines for the strict-small track of the 2025 BabyLM challenge. This specific model is trained with a equal number of examples being causal and masked. This is a pre-trained language model. It can be used to evaluate tasks zero-shot in both a causal and masked setting. It can also be fine-tuned by adding a new head and dropping the language modeling head. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance. It can also be used for mask infilling. We used the BabyLM 10M (Strict-small) dataset to train the model. It is composed in the following way:
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View details ↗babylm-baseline-10m-gpt2
BabyLM-community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 ↗babylm-interaction-baseline-simpo
BabyLM-community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 ↗babylm-multimodal-baseline-flamingo
BabyLM-communityThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
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View details ↗babylm-multimodal-baseline-git
BabyLM-communityThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗Jailbreak_Llama2-7B_BadNets
BackdoorLLM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 ↗Devstral-Small-2-24B-Instruct-2512-GGUF
backpack-run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 ↗Qwen3.5-27B-Instruct-Uncensored
backpropSukuna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 ↗Big-Lemon-Cookie-11B-GGUF
backyardai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 ↗Bionic-Cetacean-20B-GGUF
backyardai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 ↗c4ai-command-r-v01-GGUF
backyardai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 ↗Cerebrum-1.0-8x7b-GGUF
backyardai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 ↗Chaifighter-20B-v2-GGUF
backyardai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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