Create README.md

README.md

@@ -0,0 +1,126 @@
+---
+license: openrail
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+
+## Original model card 
+
+Buy me a coffee if you like this project ;)
+<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
+
+#### Description 
+
+GGML Format model files for [This project](https://huggingface.co/hiyouga/baichuan-13b-sft/tree/main).
+
+
+### inference 
+
+
+```python
+
+import ctransformers
+
+from ctransformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
+gpu_layers=32, model_type="llama")
+
+manual_input: str = "Tell me about your last dream, please."
+
+
+llm(manual_input, 
+      max_new_tokens=256, 
+      temperature=0.9, 
+      top_p= 0.7)
+
+```
+
+# Original model card
+
+A bilingual instruction-tuned LoRA model of https://huggingface.co/baichuan-inc/Baichuan-13B-Base
+
+- Instruction-following datasets used: alpaca-en, alpaca-zh, sharegpt, open assistant, lima, refgpt
+- Training framework: https://github.com/hiyouga/LLaMA-Efficient-Tuning
+
+Usage:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("hiyouga/baichuan-13b-sft", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("hiyouga/baichuan-13b-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-13b-sft
+```
+
+---
+
+You can reproduce our results by visiting the following step-by-step (Chinese) guide:
+
+https://zhuanlan.zhihu.com/p/645010851
+
+or using the following scripts in [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-13B-Base \
+    --do_train \
+    --dataset alpaca_gpt4_en,alpaca_gpt4_zh,self_cognition,oaast_sft,sharegpt_zh,refgpt_zh_p1,lima,self_cognition \
+    --template default \
+    --finetuning_type lora \
+    --lora_rank 32 \
+    --lora_target W_pack,o_proj,down_proj,up_proj,gate_proj \
+    --output_dir baichuan_13b_lora \
+    --per_device_train_batch_size 4 \
+    --gradient_accumulation_steps 8 \
+    --preprocessing_num_workers 16 \
+    --max_source_length 1024 \
+    --max_target_length 512 \
+    --optim paged_adamw_32bit \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --warmup_steps 100 \
+    --learning_rate 3e-5 \
+    --max_grad_norm 0.5 \
+    --num_train_epochs 2.0 \
+    --quantization_bit 4 \
+    --plot_loss \
+    --fp16
+```
+
+Loss curve:
+
+![loss](loss.svg)
+
+---
+
+Examples:
+
+| Question | Baichuan-13b-sft | ChatGPT |
+| -------- | ---------------- | ------- |
+| 你是谁 | 我是 \<NAME\>，一款由 \<AUTHOR\> 开发的人工智能助手。我可以回答各种问题，提供帮助和支持。 | 我是ChatGPT，一个由OpenAI开发的人工智能语言模型。我基于GPT-3.5架构训练，旨在回答各种问题和提供帮助。有什么我可以帮助你的吗？ |
+| 中国第三代战斗机的代表是什么？ | 中
+