Create README.md

README.md

@@ -0,0 +1,104 @@
+---
+library_name: transformers
+license: mit
+datasets:
+- eagle0504/augmented_codealpaca-20k-using-together-ai-deepseek-v1
+language:
+- en
+new_version: eagle0504/finetuned-deepseek-r1-distill-qwen-1.5b-by-openai-gsm8k-enhanced-v2
+pipeline_tag: question-answering
+---
+
+# Model Card for CodeAlpaca-20k Dataset Enhanced with Reasoning
+
+This model is fine-tuned to answer questions based on the CodeAlpaca-20k dataset enhanced with reasoning provided from Deepseek R1.
+
+Invoke notebook shared [here](https://colab.research.google.com/drive/1B_Fbz0w76QxHbo9zAOf_pyZKKNI0EJJ9?usp=sharing), a publicly available Colab notebook for tests.
+
+---
+
+## Model Details
+
+### Model Description
+
+This is a transformer-based question-answering model fine-tuned from `eagle0504/finetuned-deepseek-r1-distill-qwen-1.5b-by-openai-gsm8k-enhanced-v2`. It was trained on a dataset derived from the OpenAI GSM8K benchmark, enhanced with chain-of-thought reasoning to encourage intermediate logical steps. The dataset pairs math word problems with structured answers, using `<think>...</think>` and `<answer>...</answer>` tags.
+
+- **Developed by:** Yiqiao Yin
+- **Model type:** Causal Language Model (fine-tuned for Q&A with reasoning)
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** eagle0504/finetuned-deepseek-r1-distill-qwen-1.5b-by-openai-gsm8k-enhanced-v2
+
+---
+
+## Training Configuration
+
+- 🖥️ **Hardware:** Trained on a RunPod instance with:
+  - 🔥 4 x A100 SXM
+  - 🧠 146 vCPU
+  - 🧮 1144 GB RAM
+  - 💽 20 GB disk per GPU
+- 🐳 **Container Image:** `runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04`
+- ⏱️ **Total Training Time:** 2 hours
+- 💸 **Cost:** ~$7.56/hour × 2 hours = **$14+ USD**
+- ⚙️ **Zero Redundancy Optimization:** DeepSpeed Stage 1
+- 🎯 **Precision:** FP16 mixed-precision training
+
+---
+
+## Performance
+
+- **Mean token-level accuracy:** **98%**
+- Evaluation based on in-training token match accuracy over the formatted `<think>...</think><answer>...</answer>` structure.
+- Model demonstrates strong reasoning capability in multi-step arithmetic and logic problems.
+
+---
+
+## Inference Format
+
+To generate accurate completions, prompt the model in the following structure:
+
+```
+<question>Question: If Sally has 3 apples and buys 2 more, how many does she have in total?</question>
+```
+
+Be aware that this token `</question>` will prompt the answer to start with `<think>` which is trained into the model based on training data.
+
+The model will continue reasoning within `<think>...</think>` and provide a final answer inside `<answer>...</answer>`.
+
+---
+
+## Intended Use
+
+This model is intended for educational and research purposes in:
+- Chain-of-thought prompting
+- Math reasoning and logical inference
+- Question-answering with intermediate steps
+
+---
+
+## Limitations
+
+- Trained on structured synthetic data — real-world generalization may vary
+- Best performance achieved when following the exact inference format
+- Does not support multilingual inputs
+
+---
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{yin2024gsm8k,
+  author = {Yiqiao Yin},
+  title = {TBD},
+  year = 2025,
+  note = {TBD}
+}
+```
+
+## Model Card Contact
+
+Author: Yiqiao Yin
+Connect with me on [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/)