Update README.md

README.md

@@ -4,46 +4,6 @@ This is a replica of Alpaca by Stanford' tatsu
 
 Trained using the original instructions with a minor modification in FSDP mode
 
-# Other versions:
-13B: https://huggingface.co/chavinlo/alpaca-13b
-
-13B -> GPT4 : https://huggingface.co/chavinlo/gpt4-x-alpaca
-
-## Compute Used
-Trained on 4xA100s for 6H
-Donated by redmond.ai
-
-NO LORA HAS BEEN USED, this is a natively-finetuned model, hence "alpaca-native"
-
-If you are interested on more llama-based models, you can check out my profile or search for other models at https://huggingface.co/models?other=llama
-
-This (MIGHT) be a quantized version of this model, but be careful: https://boards.4channel.org/g/thread/92173062#p92182396
-
-CONFIGURATION (default except fsdp):
-
-```shell
-torchrun --nproc_per_node=4 --master_port=3045 train.py \
-    --model_name_or_path /workspace/llama-7b-hf \
-    --data_path ./alpaca_data.json \
-    --bf16 True \
-    --output_dir /workspace/output \
-    --num_train_epochs 3 \
-    --per_device_train_batch_size 4 \
-    --per_device_eval_batch_size 4 \
-    --gradient_accumulation_steps 8 \
-    --evaluation_strategy "no" \
-    --save_strategy "steps" \
-    --save_steps 200 \
-    --save_total_limit 1 \
-    --learning_rate 2e-5 \
-    --weight_decay 0. \
-    --warmup_ratio 0.03 \
-    --lr_scheduler_type "cosine" \
-    --logging_steps 1 \
-    --fsdp "shard_grad_op auto_wrap" \
-    --fsdp_transformer_layer_cls_to_wrap 'LLaMADecoderLayer' \
-    --tf32 True --report_to="wandb"
-```
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__alpaca-native)