Update instructions in readme.md

fingpt/FinGPT-Benchmark/readme.md

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 ](https://arxiv.org/abs/2310.04793)
 
 
+The datasets we used, and the multi-task financial LLMs models are available at <https://huggingface.co/FinGPT>
+
+---
+
+Before you start, make sure you have the correct versions of the key packages installed.
+```
+transformers==4.32.0
+peft==0.5.0
+```
+
+## Ready-to-use Demo
+
+For users who want ready-to-use financial multi-task language models, please refer to `demo.ipynb`.
+Following this notebook, you're able to test Llama2-7B, ChatGLM2-6B, MPT-7B, BLOOM-7B, Falcon-7B, or Qwen-7B with any of the following tasks: 
+- Financial Sentiment Analysis
+- Headline Classification
+- Named Entity Recognition
+- Financial Relation Extraction
+
+We suggest users follow the instruction template and task prompts that we used in our training process. Demos are shown in `demo.ipynb`. Due to the limited diversity of the financial tasks and datasets we used, models might not respond correctly to out-of-scope instructions. We'll delve into the generalization ability more in our future works.
+
+## Prepare Data & Base Models
+
+For the base models we used, we recommend pre-download them to `base_models/`.
+
+Refer to the `parse_model_name()` function in `utils.py` for the huggingface models we used for each LLM. (We use base models rather than any instruction-tuned version or chat version, except for ChatGLM2)
+
+---
+
+For the datasets we used, download our processed instruction tuning data from huggingface. Take FinRED dataset as an example:
+```
+import datasets
+
+dataset = datasets.load_dataset('FinGPT/fingpt-finred')
+# save to local disk space (recommended)
+dataset.save_to_disk('data/fingpt-finred')
+```
+Then `finred` became an available task option for training.
+
+We use different datasets at different phases of our instruction tuning paradigm.
+- Task-specific Instruction Tuning: `sentiment-train / finred-re / ner / headline`
+- Multi-task Instruction Tuning: `sentiment-train & finred & ner & headline`
+- Zero-shot Aimed Instruction Tuning: `finred-cls & ner-cls & headline-cls -> sentiment-cls (test)`
+
+You may download the datasets according to your needs. We also provide processed datasets for ConvFinQA and FinEval, but they are not used in our final work.
+
+### prepare data from scratch
+To prepare training data from raw data, you should follow `data/prepate_data.ipynb`. 
+
+We don't include any source data from other open-source financial datasets in our repository. So if you want to do it from scratch, you need to find the corresponding source data and put them in `data/` before you start. 
+
+---
+
+## Instruction Tuning
+
+`train.sh` contains examples of instruction tuning with this repo.
+If you don't have training data & base models in your local disk, pass `--from_remote true` in addition.
+
+### Task-specific Instruction Tuning
+```
+deepspeed train_lora.py \
+--run_name headline-chatglm2-linear \
+--base_model chatglm2 \
+--dataset headline \
+--max_length 512 \
+--batch_size 4 \
+--learning_rate 1e-4 \
+--num_epochs 8
+```
+### Multi-task Instruction Tuning
+```
+deepspeed train_lora.py \
+--run_name MT-falcon-linear \
+--base_model falcon \
+--dataset sentiment-train,headline,finred*3,ner*15 \
+--max_length 512 \
+--batch_size 4 \
+--learning_rate 1e-4 \
+--num_epochs 4
+```
+### Zero-shot Aimed Instruction Tuning
+```
+deepspeed train_lora.py \
+--run_name GRCLS-sentiment-falcon-linear-small \
+--base_model falcon \
+--test_dataset sentiment-cls-instruct \
+--dataset headline-cls-instruct,finred-cls-instruct*2,ner-cls-instruct*7 \
+--max_length 512 \
+--batch_size 4 \
+--learning_rate 1e-4 \
+--num_epochs 1 \
+--log_interval 10 \
+--warmup_ratio 0 \
+--scheduler linear \
+--evaluation_strategy steps \
+--eval_steps 100 \
+--ds_config config_hf.json
+```
+
+---
+
+## Evaluation for Financial Tasks
+
+Refer to `Benchmarks/evaluate.sh` for evaluation script on all Financial Tasks.
+You can evaluate your trained model on multiple tasks together. For example:
+```
+python benchmarks.py \
+--dataset fpb,fiqa,tfns,nwgi,headline,ner,re \
+--base_model llama2 \
+--peft_model ../finetuned_models/MT-llama2-linear_202309241345 \
+--batch_size 8 \
+--max_length 512
+```
+
+For Zero-shot Evaluation on Sentiment Analysis, we use multiple prompts and evaluate each of them.
+The task indicators are `fiqa_mlt` and `fpb_mlt`.
+
+