Restore README.md

README.md

@@ -1,110 +1,120 @@
 ---
 license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
-language:
-- en
-pipeline_tag: text-generation
-base_model: Qwen/Qwen2.5-0.5B
-tags:
-- chat
-library_name: transformers
 ---
+# 🏷️ EAI-Distill-0.5b
 
-# Qwen2.5-0.5B-Instruct
+## 📋 Model Description
 
-## Introduction
+EAI-Distill-0.5b is a fine-tuned version of Qwen2.5-0.5B-Instruct designed for document classification across 12 taxonomic categories. This model is optimized for high-throughput classification of web documents and produces structured metadata for large-scale dataset curation.
 
-Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
+The model classifies documents across the following dimensions:
+- **📚 Free Decimal Correspondence (FDC)**: Subject matter classification based on the Dewey Decimal System
+- **🧠 Bloom's Taxonomy**: Cognitive process (Remember/Understand/Apply/Analyze/Evaluate/Create) and knowledge domain (Factual/Conceptual/Procedural/Metacognitive)
+- **📄 Document Type**: Web page categorization (News, Academic, Reference, Code, Social, etc.)
+- **🔍 Content Quality**: Extraction artifacts, missing content detection
+- **🎓 Educational Metadata**: Reasoning depth, technical correctness, educational level
 
-- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
-- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
-- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
-- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 
+## 🚀 Training Details
 
-**This repo contains the instruction-tuned 0.5B Qwen2.5 model**, which has the following features:
-- Type: Causal Language Models
-- Training Stage: Pretraining & Post-training
-- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
-- Number of Parameters: 0.49B
-- Number of Paramaters (Non-Embedding): 0.36B
-- Number of Layers: 24
-- Number of Attention Heads (GQA): 14 for Q and 2 for KV
-- Context Length: Full 32,768 tokens and generation 8192 tokens
+- **🤖 Base Model**: Qwen2.5-0.5B-Instruct
+- **📊 Training Data**: 82B synthetic tokens generated by Qwen2.5-32B-Instruct (teacher model) on 104M Common Crawl documents
+- **⚙️ Optimizer**: AdamW (β₁=0.9, β₂=0.95, weight_decay=0.1)
+- **📈 Learning Rate**: 1×10⁻⁴ with linear warmup (2B tokens), cosine decay to 1×10⁻⁵, then linear anneal to 0
+- **📦 Batch Size**: 2M tokens
+- **📏 Sequence Length**: 16,384 tokens
+- **💻 Hardware**: Trained on AMD MI300x GPUs
 
-For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+## 📊 Performance
 
-## Requirements
-
-The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
-
-With `transformers<4.37.0`, you will encounter the following error:
-```
-KeyError: 'qwen2'
-```
-
-## Quickstart
-
-Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+The model achieves an average Cohen's κ agreement of 0.71-0.74 with our golden annotators, GPT-4o and Claude 3.5 Sonnet, on held-out evaluation sets, which is within 3% of its teacher model Qwen2.5-32b-Instruct while being 64× smaller.
+## 💻 Usage
 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "Qwen/Qwen2.5-0.5B-Instruct"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Give me a short introduction to large language model."
-messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    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]
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import random
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("EssentialAI/EAI-Distill-0.5b", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("EssentialAI/EAI-Distill-0.5b")
+
+def chunk_text(text, max_char_per_doc=30000):
+    if len(text) <= max_char_per_doc:
+        return text
+        
+    chunk_size = max_char_per_doc // 3
+    start = text[:chunk_size]
+    
+    middle_start = chunk_size 
+    middle_end = len(text) - chunk_size 
+    
+    mid_point = random.randint(middle_start + chunk_size//2, middle_end - chunk_size//2)
+    
+    middle = text[mid_point - chunk_size//2:mid_point + chunk_size//2]
+    end = text[-chunk_size:]
+    return f"[beginning]\n{start}\n[middle]\n{middle}\n[end]\n{end}"
+
+def classify_document(text):
+    chunked_text = chunk_text(text)
+    
+    messages = [
+        {"role": "system", "content": "taxonomy"},
+        {"role": "user", "content": chunked_text},
+    ]
+    
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example usage
+document_text = "Your document content here..."
+classification = classify_document(document_text)
+print(classification)
 ```
 
+## 📤 Output Format
 
-## Evaluation & Performance
+The model outputs classifications in a condensed format:
+```
+{FDC primary},{FDC secondary or skip}
+{Bloom cognitive process primary (1-6)},{Bloom cognitive process secondary (1-6) or skip}
+{Bloom knowledge domain primary (1-4)},{Bloom knowledge domain secondary (1-4) or skip}
+{Document type v1 primary (1-17)},{Document type v1 secondary (1-17) or skip}
+{Extraction artifacts primary (0-4)},{Extraction artifacts secondary (0-4) or skip}
+{Missing content primary (0-6)},{Missing content secondary (0-6) or skip}
+{Document type v2 primary (1-25)},{Document type v2 secondary (1-25) or skip}
+{Reasoning depth primary (1-6)},{Reasoning depth secondary (1-6) or skip}
+{Technical correctness primary (1-6)},{Technical correctness secondary (1-6) or skip}
+{Educational level primary (1-5)},{Educational level secondary (1-5) or skip}
+```
 
-Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
+## 🎯 Intended Use
 
-For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+This model is designed for:
+- 🏗️ Large-scale web document classification and metadata generation
+- 🔧 Dataset curation through taxonomic filtering
+- ✅ Content quality assessment for training data preparation
+- 📚 Educational content analysis and organization
 
-## Citation
+## ⚠️ Limitations
 
-If you find our work helpful, feel free to give us a cite.
+- Optimized for English web documents extracted using resiliparse
+- Documents over 30k characters are automatically chunked, which may affect classification accuracy
+- Performance may vary on content significantly different from Common Crawl web data
+- Classification categories are based on web content patterns and may not generalize to other document types
 
-```
-@misc{qwen2.5,
-    title = {Qwen2.5: A Party of Foundation Models},
-    url = {https://qwenlm.github.io/blog/qwen2.5/},
-    author = {Qwen Team},
-    month = {September},
-    year = {2024}
-}
+## 📝 Citation
 
-@article{qwen2,
-      title={Qwen2 Technical Report}, 
-      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
-      journal={arXiv preprint arXiv:2407.10671},
-      year={2024}
+If you use this model, please cite:
+```bibtex
+@article{essential-web-2024,
+  title={Essential-Web: A 24-Trillion Token Dataset with Extensive Metadata for Training LLMs},
+  author={[Your Authors]},
+  year={2024}
 }
 ```