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## Training
1. Clone the TencentPretrain project and install dependencies: PyTorch, DeepSpeed, SentencePiece
```
git clone https://github.com/Tencent/TencentPretrain.git
```
2. Convert LLaMA-7B weights to TencentPretrain format
```
cd TencentPretrain
python3 scripts/convert_llama_to_tencentpretrain.py --input_model_path $LLaMA_7B_FOLDER/consolidated.00.pth --output_model_path models/llama-7b.bin --layers_num 32
```
3. Modify configuration file
Check out the `tencentpretrain/utils/constants.py` file, and modify L4: `special_tokens_map.json` to `llama_special_tokens_map.json`
4. Data preprocess. We use the example corpus in the project for pre-training, one can also use custom data training in the same format (one sample per line).
```
python3 preprocess.py --corpus_path corpora/book_review.txt --spm_model_path $LLaMA_7B_FOLDER/tokenizer.model \
--dataset_path dataset.pt --processes_num 8 --data_processor lm
```
5. Start training.
```
deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_config.json \
--pretrained_model_path models/llama-7b.bin \
--dataset_path dataset.pt --spm_model_path $LLaMA_7B_FOLDER/tokenizer.model \
--config_path models/llama/7b_config.json \
--output_model_path models/output_model.bin \
--world_size 8 --learning_rate 1e-4 \
--data_processor lm --total_steps 10000 --save_checkpoint_steps 2000 --batch_size 24
```
## Inference
Similar to facebookresearch/llama, TencentPretrain also provides language model inference code.
For example, using a single GPU for LLaMA-7B inference, the prompt is in the file `beginning.txt`:
```
python3 scripts/generate_lm.py --load_model_path models/llama-7b.bin --spm_model_path $LLaMA_7B_FOLDER/tokenizer.model \
--test_path beginning.txt --prediction_path generated_sentence.txt \
--config_path models/llama/7b_config.json
```
For now, TencentPretrain only support LLaMA-7B training. We are working on our framework to support LLaMA model training/fine-tuning at all scales and sharing more experimental results.