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distilgpt2

distilbert

DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, training, and limitations of GPT-2. - **Developed by:** Hugging Face - **Model type:** Transformer-based Language Model - **Language:** English - **License:** Apache 2.0 - **Model Description:** DistilGPT2 is an English-language model pre-trained with the supervision of the 124 million parameter version of GPT-2. DistilGPT2, which has 82 million parameters, was developed using knowledge distillation and was designed to be a faster, lighter version of GPT-2. - **Resources for more information:** See this repository for more about Distil\* (a class of compressed models including Distilled-GPT2), Sanh et al. (2019) for more information about knowledge distillation and the training procedure, and this page for more about GPT-2. **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**

Text generationOpen weights
Input Text→ Output Text
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1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. How To Get Started With the Model This model is a distilled version of the RoBERTa-base model. It follows the same training procedure as DistilBERT. The code for the distillation process can be found here. This model is case-sensitive: it makes a difference between english and English. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.

Text generationOpen weights
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Visual QA
Input Text · Image→ Output Text
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Visual QA
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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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Visual QA
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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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llama-1b-run-7

distributed

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This model uses per layer fine grained quantization. What dtype to use for a layer is selected dynamically by trial and error until the std normalized mse loss is lower than the selected threshold. Minimum allowed dtype is set to uint4 and std normalized mse loss threshold is set to 1e-2. This created a mixed precision model with uint4 and int5 dtypes. SVD quantization is disabled.

Image generation
Input Text→ Output Image
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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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t5-xxl

Disty0

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