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license: apache-2.0
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base_model:https://huggingface.co/google/gemma-2b

the language of model: chinese and english

The following uses gemma-2b (a language model that only supports English) to train a large model process that supports Chinese and English.

step 1:
Use SentencePiece(bpe) to train Chinese corpus to obtain tokenizer.model and tokenizer.vocab

step 2：
Merge the Chinese of tokenizer.model and the original of tokenizer.model

step 3：
Use the merged special_tokens_map.json, tokenizer.model, tokenizer_config.json to replace the files of the original model (such as gemma-2b)

step 4:
Use LLaMA-Factory for pre-training. Pay attention to the pre-training parameters. Resize vocab and resize embedding are required.

step 5:
Based on the model pre-trained in step 4, the instructions are fine-tuned, which significantly improves the model's ability to understand and execute instructions.

step 6:
Based on the instruction fine-tuning model, we can use this model for SFT training under different specific tasks, so that the model can perform better on specific tasks.