Add README with usage instructions

README.md

@@ -1,55 +1,45 @@
 ---
 license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- qwen
+- qwen2.5
+- instruct
+- runpod
+- serverless
 language:
 - en
+- zh
 pipeline_tag: text-generation
-base_model: Qwen/Qwen2.5-0.5B
-tags:
-- chat
-library_name: transformers
 ---
 
-# Qwen2.5-0.5B-Instruct
-
-## Introduction
+# Qwen2.5-0.5B-Instruct (Customizable Copy)
 
-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:
+This is a copy of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) for customization and fine-tuning.
 
-- 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. 
+## 📋 Model 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:** Qwen/Qwen2.5-0.5B-Instruct
+- **Size:** 0.5B parameters (~1GB)
+- **Type:** Instruction-tuned language model
+- **License:** Apache 2.0
 
-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/).
+## 🎯 Purpose
 
-## 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'
-```
+This repository contains a **modifiable copy** of Qwen 2.5 for:
+- Fine-tuning on custom datasets
+- Experimentation and testing
+- RunPod serverless deployment
+- Model modifications
 
-## Quickstart
+## 🚀 Usage
 
-Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+### Direct Inference
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "Qwen/Qwen2.5-0.5B-Instruct"
+model_name = "marcosremar2/runpod_serverless_n2"
 
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -58,53 +48,81 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-prompt = "Give me a short introduction to large language model."
+prompt = "What is artificial intelligence?"
 messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "system", "content": "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]
+print(response)
 ```
 
+### RunPod Serverless Deployment
 
-## Evaluation & Performance
+```yaml
+Environment Variables:
+  MODEL_NAME: marcosremar2/runpod_serverless_n2
+  HF_TOKEN: YOUR_TOKEN_HERE
+  MAX_MODEL_LEN: 4096
+  TRUST_REMOTE_CODE: true
 
-Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
+GPU: RTX 4090 (24GB)
+Min Workers: 0
+Max Workers: 1
+```
 
-For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+## 🔧 Fine-tuning
 
-## Citation
+To fine-tune this model:
 
-If you find our work helpful, feel free to give us a cite.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
 
+model = AutoModelForCausalLM.from_pretrained("marcosremar2/runpod_serverless_n2")
+tokenizer = AutoTokenizer.from_pretrained("marcosremar2/runpod_serverless_n2")
+
+# Your fine-tuning code here
+# ...
+
+# Push back to your repo
+model.push_to_hub("marcosremar2/runpod_serverless_n2")
+tokenizer.push_to_hub("marcosremar2/runpod_serverless_n2")
 ```
-@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}
-}
-
-@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}
-}
-```
+
+## 📊 Performance
+
+| Metric | Value |
+|--------|-------|
+| Parameters | 0.5B |
+| Context Length | 32K tokens |
+| VRAM Required | ~1-2GB |
+| Inference Speed | 200-300 tokens/sec (RTX 4090) |
+
+## 🔗 Original Model
+
+This is based on: [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+
+For more information about the Qwen2.5 series, visit the original repository.
+
+## 📄 License
+
+Apache 2.0 - Same as the original Qwen model.
+
+## 🙏 Credits
+
+- **Original Model:** Qwen Team @ Alibaba Cloud
+- **Repository:** Custom copy for modification and deployment