Update README.md

README.md

@@ -1,3 +1,73 @@
 ---
 license: apache-2.0
+datasets:
+- pankajmathur/WizardLM_Orca
+language:
+- en
+pipeline_tag: text-generation
 ---
+---
+base_model: mistralai/Mistral-7B-v0.1
+---
+
+## Reverse Instruct LoRa Adapter
+
+This LoRa Adapter is fine tuned to reverse engineer the original prompt of a given LLM output/response.
+
+## Response Format
+
+"[INST]\n### System:\n{system}\n### Instruction:\n{instruction}\n[/INST]\n"
+
+(without the "")
+
+## Prompt Template
+
+"\n### System:\nYou craft instructions for generating the given output through reverse engineering.\n### Instruction:\nDecipher the steps used to produce the given output and articulate a refined set of instructions (System & Instruction).\n### OUTPUT:\n {output}"
+
+(use the template without the " ")
+
+## Training Dataset
+
+About 21k items of the following datasets were used. (mostly coding-like tasks were removed)
+
+```bash
+wget https://raw.githubusercontent.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/main/data/alpaca_gpt4_data.json
+wget https://raw.githubusercontent.com/teknium1/GPTeacher/main/Roleplay%20Supplemental/roleplay-instruct-v2.1.json
+wget https://huggingface.co/datasets/pankajmathur/WizardLM_Orca/resolve/main/wizardlm_orca.json
+```
+
+## Training Procedure
+
+```bash
+CUDA_VISIBLE_DEVICES=0 WANDB_DISABLED=True python LLaMA-Factory/src/train_bash.py \
+    --stage sft \
+    --model_name_or_path model_name_or_path \
+    --checkpoint_dir checkpoint_dir \
+    --do_train \
+    --dataset default \
+    --template vanilla \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --output_dir path_to_sft_checkpoint \
+    --overwrite_cache \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 1 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 100 \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --plot_loss \
+    --fp16 \
+    --overwrite_output_dir \
+    --cutoff_len 2048 \
+    --quantization_bit 4
+```
+
+## Training Time
+
+- ~30h on Kaggle's P100 GPU
+
+### Framework versions
+
+- LLaMA-Factory