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gemma-2b-bnb-4bit

unsloth

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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All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. - This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. - This text completion notebook is for raw text. This DPO notebook replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

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gemma-2b-it-bnb-4bit

unsloth

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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요금 미확인멀티모달 및 채널 요금 별도 확인

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gemma-3-12b-it

unsloth

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components:

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요금 미확인멀티모달 및 채널 요금 별도 확인

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gemma-3-12b-it-bnb-4bit

unsloth

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

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gemma-3-12b-it-FP8-Dynamic

unsloth

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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gemma-3-12b-it-GGUF

unsloth

See our collection for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats. - Fine-tune Gemma 3 (12B) for free using our Google Colab notebook here! - Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3 - View the rest of our notebooks in our docs here. - Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3]

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-12b-it-qat-bnb-4bit

unsloth

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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요금 미확인멀티모달 및 채널 요금 별도 확인

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gemma-3-12b-it-qat-GGUF

unsloth

> [!Note] > This repository corresponds to the 12B **instruction-tuned** version of the Gemma 3 model using Quantization Aware Training (QAT). > > **The checkpoint in this repository is unquantized, please make sure to quantize with Q4_0 with your favorite tool** > > Thanks to QAT, the model is able to preserve similar quality as `bfloat16` while significantly reducing the memory requirements > to load the model. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-12b-it-qat-int4-GGUF

unsloth

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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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-12b-it-unsloth-bnb-4bit

unsloth

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-12b-pt

unsloth

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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gemma-3-1b-it

unsloth

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-1b-it-bnb-4bit

unsloth

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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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-1b-it-GGUF

unsloth

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-1b-it-unsloth-bnb-4bit

unsloth

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-1b-pt

unsloth

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-1b-pt-unsloth-bnb-4bit

unsloth

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。

기본 정보 등록 · 상세 정보 보완 예정

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-270m

unsloth

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens, the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The knowledge cutoff date for the training data was August 2024. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-270m-it

unsloth

See our collection for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats. - Fine-tune Gemma 3 (270M) for free using our Google Colab notebook here.ipynb)! - Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3 - View the rest of our notebooks in our docs here. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3]

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-270m-it-bnb-4bit

unsloth

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。

기본 정보 등록 · 상세 정보 보완 예정

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요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

gemma-3-270m-it-GGUF

unsloth

> [!NOTE] > Please use the correct settings: `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` > See our collection for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats. - Fine-tune Gemma 3 (270M) for free using our Google Colab notebook here.ipynb)! - Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3 - View the rest of our notebooks in our docs here.

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요금 미확인멀티모달 및 채널 요금 별도 확인