Create README.md

README.md

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+A bilingual instruction-tuned LoRA model of https://huggingface.co/baichuan-inc/baichuan-7B
+
+- Instruction-following datasets used: alpaca, alpaca-zh, codealpaca
+- Training framework: https://github.com/hiyouga/LLaMA-Efficient-Tuning
+
+Please follow the [baichuan-7B License](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) to use this model.
+
+Usage:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("hiyouga/baichuan-7b-sft", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("hiyouga/baichuan-7b-sft", trust_remote_code=True).cuda()
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+query = "晚上睡不着怎么办"
+template = (
+    "A chat between a curious user and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
+    "Human: {}\nAssistant: "
+)
+
+inputs = tokenizer([template.format(query)], return_tensors="pt")
+inputs = inputs.to("cuda")
+generate_ids = model.generate(**inputs, max_new_tokens=256, streamer=streamer)
+```
+
+You could also alternatively launch a CLI demo by using the script in https://github.com/hiyouga/LLaMA-Efficient-Tuning
+
+```bash
+python src/cli_demo.py --template default --model_name_or_path hiyouga/baichuan-7b-sft
+```
+
+---
+
+You could reproduce our results with the following scripts using [LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning):
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
+    --stage sft \
+    --model_name_or_path baichuan-inc/baichuan-7B \
+    --do_train \
+    --dataset alpaca_gpt4_en,alpaca_gpt4_zh,codealpaca \
+    --template default \
+    --finetuning_type lora \
+    --lora_rank 16 \
+    --lora_target W_pack,o_proj,gate_proj,down_proj,up_proj \
+    --output_dir baichuan_lora \
+    --overwrite_cache \
+    --per_device_train_batch_size 8 \
+    --per_device_eval_batch_size 8 \
+    --gradient_accumulation_steps 8 \
+    --preprocessing_num_workers 16 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --learning_rate 5e-5 \
+    --max_grad_norm 0.5 \
+    --num_train_epochs 2.0 \
+    --dev_ratio 0.01 \
+    --evaluation_strategy steps \
+    --load_best_model_at_end \
+    --plot_loss \
+    --fp16
+```
+
+Loss curve on training set:
+![train](assets/training_loss.svg)
+
+Loss curve on evaluation set:
+![eval](assets/eval_loss.svg)