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Expand Up @@ -54,6 +54,32 @@ To train the models, run `accelerate config` and enable DeepSpeed acceleration.
The tokenized training data is available on [🤗Hugging Face](https://huggingface.co/datasets/emozilla/pg_books-tokenized-bos-eos-chunked-65536) and was derived from the [pg19](https://huggingface.co/datasets/emozilla/pg19) dataset.
For the Mistral models, a mix of the pretrain and fine-tune splits of [Long-Data-Collections](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections) was used and the tokenized dataset is also available on [🤗Hugging Face](https://huggingface.co/datasets/emozilla/yarn-train-tokenized-16k-mistral).

Here is a more dedicated fast try for beginners, take Llama-2-7b-8k as example, it may need 4 hours on 4xA100:
```sh
# **Step1.** Accelerate config
$ accelerate config
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
In which compute environment are you running?
This machine
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Which type of machine are you using?
multi-GPU
How many different machines will you use (use more than 1 for multi-node training)? [1]:
Should distributed operations be checked while running for errors? This can avoid timeout issues but will be slower. [yes/NO]:
Do you wish to optimize your script with torch dynamo?[yes/NO]:
Do you want to use DeepSpeed? [yes/NO]: yes
Do you want to specify a json file to a DeepSpeed config? [yes/NO]: yes
Please enter the path to the json DeepSpeed config file: /workspace/yarn/deepspeed/zero3.json
Do you want to enable `deepspeed.zero.Init` when using ZeRO Stage-3 for constructing massive models? [yes/NO]: yes
How many GPU(s) should be used for distributed training? [1]:4
accelerate configuration saved at /root/.cache/huggingface/accelerate/default_config.yaml

# **Step2.** Modify deepspeed/zero3.json according to [deepspeed configuration json](https://www.deepspeed.ai/docs/config-json/) in case of OOM

# **Step3.** Enable wandb and train
$ accelerate launch finetune.py --output-dir output/yarn-7b-8k --model NousResearch/Llama-2-7b-hf --scaling-factor 2 --wandb ${YOUR_WANDB_PROJECT} --dataset emozilla/yarn-train-tokenized-8k-llama --deepspeed
```

### Evaluation

To reproduce the evaluations, install [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) with `pip install git+https://github.com/EleutherAI/lm-evaluation-harness` and then run the two provided scripts.
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