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@@ -33,11 +33,11 @@ Currently pad token by default in [HuggingFace Tokenizer is `None`](https://gith
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```python
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tokenizer.add_special_tokens(
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{
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-
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+
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"pad_token": "<PAD>",
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}
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)
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-model.resize_token_embeddings(model.config.vocab_size + 1)
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+model.resize_token_embeddings(model.config.vocab_size + 1)
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```
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Padding would be required for batch inference. In this this [example](../inference/inference.py), batch size = 1 so essentially padding is not required. However,We added the code pointer as an example in case of batch inference.
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@@ -99,7 +99,7 @@ In case you have fine-tuned your model with pure FSDP and saved the checkpoints
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This is helpful if you have fine-tuned you model using FSDP only as follows:
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```bash
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-torchrun --nnodes 1 --nproc_per_node 8 llama_finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16
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+torchrun --nnodes 1 --nproc_per_node 8 llama_finetuning.py --enable_fsdp --model_name /patht_of_model_folder/7B --dist_checkpoint_root_folder model_checkpoints --dist_checkpoint_folder fine-tuned --pure_bf16
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```
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Then convert your FSDP checkpoint to HuggingFace checkpoints using:
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```bash
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@@ -112,10 +112,22 @@ By default, training parameter are saved in `train_params.yaml` in the path wher
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Then run inference using:
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```bash
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-python inference/inference.py --model_name <training_config.output_dir> --prompt_file <test_prompt_file>
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+python inference/inference.py --model_name <training_config.output_dir> --prompt_file <test_prompt_file>
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```
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+## Prompt Llama 2
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+
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+As outlined by [this blog by Hugging Face](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), you can use the template below to prompt Llama 2 chat models. Review the [blog article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) for more information.
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+
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+```
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+<s>[INST] <<SYS>>
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+{{ system_prompt }}
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+<</SYS>>
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+
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+{{ user_message }} [/INST]
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+
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+```
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## Other Inference Options
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