DCLM-7B
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DCLM 7B
Overview :
DCLM-Baseline-7B is a 700 million parameter language model developed by the DataComp for Language Models (DCLM) team, primarily in English. The model aims to improve the performance of language models by using systematic data organization technology. The model training uses PyTorch and the OpenLM framework, the optimizer is AdamW, the learning rate is 2e-3, the weight decay is 0.05, the batch size is 2048 sequences, the sequence length is 2048 tokens, and the total training tokens has reached 2.5 trillion. The training hardware uses the H100 GPU.
Target Users :
DCLM-7B model is suitable for researchers and developers who need to perform large-scale language processing and generation, especially in scenarios where English data needs to be processed. Its large-scale parameters and systematic data organization technology make it advantageous in improving language model performance.
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Use Cases
Researchers evaluate the DCLM-7B model for zero-shot (zero-shot) and few-shot (few-shot) learning.
Developers use the model to improve performance in applications such as question-answering systems and text generation.
Educators use the DCLM-7B model to teach and demonstrate the principles and applications of language models.
How to Use
Install the open_lm library first.
Import the necessary modules and classes, including AutoTokenizer and AutoModelForCausalLM.
Use AutoTokenizer to load the tokenizer from the pretrained model.
Use AutoModelForCausalLM to load the model from the pretrained model.
Prepare the input data and convert it to the format required by the model.
Set the generation parameters such as max_new_tokens, top_p, etc.
Call the generate method of the model to generate text.
Use the tokenizer to decode the generated text and print the output.
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