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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
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+[Discord](https://discord.gg/pvy7H8DZMG)
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+[Request more models](https://github.com/RichardErkhov/quant_request)
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+
+Tongda1-1.5B-BKI - AWQ
+- Model creator: https://huggingface.co/Tongda/
+- Original model: https://huggingface.co/Tongda/Tongda1-1.5B-BKI/
+
+
+
+
+Original model description:
+---
+license: apache-2.0
+datasets:
+- Tongda/bid-announcement-zh-v1.0
+base_model:
+- Qwen/Qwen2-1.5B-Instruct
+pipeline_tag: text-generation
+tags:
+- text-generation-inference
+library_name: transformers
+---
+
+
+## **Model Overview**
+
+This model is a fine-tuned version of the Qwen2-1.5-Instruct using Low-Rank Adaptation (LoRA). It is specifically designed for extracting key information from bidding and bid-winning announcements. The model focuses on identifying structured data such as project names, announcement types, budget amounts, and deadlines in various formats of bidding notices.
+
+The base model, Qwen2-1.5-Instruct, is a large-scale language model optimized for instruction-following tasks, and this fine-tuned version leverages its capabilities for precise data extraction tasks in Chinese bid announcement contexts.
+
+---
+
+## **Use Cases**
+
+The model can be used in applications that require the automatic extraction of structured data from text documents, particularly related to government bidding and procurement processes. For instance, based on [the sample announcement](https://www.qhggzyjy.gov.cn/ggzy/jyxx/001002/001002001/20240827/1358880795267533.html), the generated output is as follows:
+
+```
+项目名称："大通县公安局警用无人自动化机场项目"
+公告类型："采购公告-竞磋"
+行业分类："其他"
+发布时间："2024-08-27"
+预算金额："941500.00元"
+采购人："大通县公安局（本级）"
+响应文件截至提交时间："2024-09-10 09:00"
+开标地址："大通县政府采购服务中心"
+所在地区："青海省"
+```
+---
+
+## **Key Features**
+
+1. **Fine-tuned with LoRA**: The model has been adapted using LoRA, a parameter-efficient fine-tuning method, allowing it to focus on specific tasks while maintaining the power of the large base model.
+   
+2. **Robust Information Extraction**: The model is trained to extract and validate crucial fields, including budget values, submission deadlines, and industry classifications, ensuring accurate outputs even when encountering variable formats.
+
+3. **Language & Domain Specificity**: The model excels in parsing official bidding announcements in Chinese and accurately extracting the required information for downstream processes.
+
+---
+
+## **Model Architecture**
+
+- **Base Model**: Qwen2-1.5B-Instruct
+- **Fine-Tuning Technique**: LoRA
+- **Training Data**: Fine-tuned on structured and unstructured government bidding announcements
+- **Framework**: Hugging Face Transformers & PEFT (Parameter Efficient Fine Tuning)
+  
+## **Technical Specifications**
+
+- **Device Compatibility**: CUDA (GPU-enabled)
+- **Tokenization**: Utilizes `AutoTokenizer` from Hugging Face, optimized for instruction-following tasks.
+
+## **Requirements**
+
+```shell
+pip install --upgrade 'transformers>=4.44.2' 'torch>=2.0' accelerate
+```
+
+## **Usage Example**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+device = "cuda" 
+
+model = AutoModelForCausalLM.from_pretrained("Tongda/Tongda1-1.5B-BKI", device_map="auto", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained("Tongda/Tongda1-1.5B-BKI")
+
+model.eval()
+
+instruction = "分析给定的公告，提取其中的“项目名称”、“公告类型”、“行业分类”、“发布时间”、“预算金额”、“采购人”、“响应文件截至提交时间”、”开标地址“、“所在地区”，并将其以json格式进行输出。如果公告出现“最高投标限价”相关的值，则“预算金额”为该值。请再三确认提取的值为项目的“预算金额”，而不是其他和“预算金额”无关的数值，否则“预算金额”中填入'None'。如果确认提取到了“预算金额”，请重点确认提取到的金额的单位，所有的“预算金额”单位为“元”。当涉及到进制转换的计算（比如“万元”转换为“元”单位）时，必须进行进制转换。其中“公告类型”只能从以下12类中挑选：采购公告-招标、采购公告-邀标、采购公告-询价、采购公告-竞谈、采购公告-竞磋、采购公告-竞价、采购公告-单一来源、采购公告-变更、采购结果-中标、采购结果-终止、采购结果-废标、采购结果-合同。其中，“行业分类”只能从以下12类中挑选：建筑与基础设施、信息技术与通信、能源与环保、交通与物流、金融与保险、医疗与健康、教育与文化、农业与林业、制造与工业、政府与公共事业、旅游与娱乐、其他。"
+
+# the content of any bid announcement
+input_report = "#### 通答产业园区(2024���-2027年）智能一体化项目公开招标公告..."
+
+messages = [
+    {"role": "system", "content": instruction},
+    {"role": "user", "content": input_report}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512
+)
+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]
+
+response
+```
+
+---
+## **Evaluation & Performance**
+
+The Tongda1-1.5B-BKI model has shown remarkable performance in information extraction tasks. Compared to the baseline model Qwen2-1.5B-Instruct, Tongda1-1.5B-BKI excels across multiple evaluation metrics, particularly in extracting key information from tender announcements, achieving significant improvements. Even when compared to larger models like Qwen2.5-3B-Instruct and Qwen2-7B-Instruct, Tongda1-1.5B-BKI still demonstrates outstanding performance. Additionally, it outperforms the optimized online model glm-4-flash. Here are the evaluation results for each model:
+
+| Model                  | ROUGE-1 | ROUGE-2 | ROUGE-Lsum | BLEU  |
+|-----------------------|---------|---------|------------|-------|
+| Tongda1-1.5B-BKI      | 0.853   | 0.787   | 0.853      | 0.852 |
+| Qwen2-1.5B-Instruct   | 0.412   | 0.231   | 0.411      | 0.431 |
+| Qwen2.5-3B-Instruct   | 0.686   | 0.578   | 0.687      | 0.755 |
+| Qwen2-7B-Instruct     | 0.703   | 0.578   | 0.703      | 0.789 |
+| glm-4-flash           | 0.774   | 0.655   | 0.775      | 0.816 |
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65ebcbd0c8577b39464e6dc0/Qiyi7onDe99b2USArl0oG.png)
+
+---
+
+## **Limitations**
+
+- **Language Limitation**: The model is primarily trained on Chinese bidding announcements. Performance on other languages or non-bidding content may be limited.
+- **Strict Formatting**: The model may have reduced accuracy when the bidding announcements deviate significantly from common structures.
+
+---
+
+## **Citation**
+If you use this model, please consider citing it as follows:
+
+```
+@inproceedings{Tongda1-1.5B-BKI,
+  title={Tongda1-1.5B-BKI: LoRA Fine-tuned Model for Bidding Announcements},
+  author={Ted-Z},
+  year={2024}
+}
+```
+
+## **Contact**
+For further inquiries or fine-tuning services, please contact us at [Tongda](https://www.tongdaai.com/).
+