Update README.md

README.md

@@ -3,20 +3,76 @@ base_model: XGenerationLab/XiYanSQL-QwenCoder-7B-2502
 tags:
 - text-generation-inference
 - transformers
-- unsloth
-- qwen2
-- trl
 license: apache-2.0
-language:
-- en
 ---
 
-# Uploaded  model
 
-- **Developed by:** beyoru
-- **License:** apache-2.0
-- **Finetuned from model :** XGenerationLab/XiYanSQL-QwenCoder-7B-2502
+# Notes in "XGenerationLab/XiYanSQL-QwenCoder-7B-2502":
+## Requirements
+```
+transformers >= 4.37.0
+```
+## Quickstart
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+Here is a simple code snippet for quickly using **XiYanSQL-QwenCoder** model. We provide a Chinese version of the prompt, and you just need to replace the placeholders for "question," "db_schema," and "evidence" to get started. We recommend using our [M-Schema](https://github.com/XGenerationLab/M-Schema) format for the schema; other formats such as DDL are also acceptable, but they may affect performance.
+Currently, we mainly support mainstream dialects like SQLite, PostgreSQL, and MySQL.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+```
+
+nl2sqlite_template_cn = """你是一名{dialect}专家，现在需要阅读并理解下面的【数据库schema】描述，以及可能用到的【参考信息】，并运用{dialect}知识生成sql语句回答【用户问题】。
+【用户问题】
+{question}
+
+【数据库schema】
+{db_schema}
+
+【参考信息】
+{evidence}
+
+【用户问题】
+{question}
+
+```sql"""
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "beyoru/QwenCoderSQL_bnb_4bit"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+## dialects -> ['SQLite', 'PostgreSQL', 'MySQL']
+prompt = nl2sqlite_template_cn.format(dialect="", db_schema="", question="", evidence="")
+message = [{'role': 'user', 'content': prompt}]
+
+text = tokenizer.apply_chat_template(
+    message,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+    max_new_tokens=1024,
+    temperature=0.1,
+    top_p=0.8,
+    do_sample=True,
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+```
+
+
+## Acknowledgments
+If you find our work useful, please give us a citation or a like, so we can make a greater contribution to the open-source community!