Update README.md

README.md

@@ -1,3 +1,50 @@
 ---
 license: apache-2.0
 ---
+language:
+- zh
+- en
+library_name: transformers
+tags:
+- baichuan
+---
+
+This is an SFT model trained using https://github.com/hiyouga/LLaMA-Efficient-Tuning.
+
+Thanks to the original author for their hard work.
+
+All work is based on https://huggingface.co/baichuan-inc/baichuan-7B.
+
+You can find the matching data set on the github of the fine-tuning framework.
+
+We carried out 4 epoch of distributed training on the 8-card H100 machine, which took a short time. However, there is not much change in the loss.
+In the future, we will update the data set to see how it will perform in a vertical field.
+
+Of course, this is the inference code of the original author. You can use it directly.
+
+Usage:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+from peft import PeftModel
+
+
+tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/baichuan-7B", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("baichuan-inc/baichuan-7B", device_map="auto", trust_remote_code=True)
+model = PeftModel.from_pretrained(model, "/data/baichuan-7b-sft") #change to your own path.
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+query = "晚上睡不着怎么办"
+
+inputs = tokenizer(["<human>:{}\n<bot>:".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 \
+    --model_name_or_path baichuan-inc/baichuan-7B \
+    --checkpoint_dir hiyouga/baichuan-7b-sft \
+
+```