Added README.md

README.md

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+---
+base_model: Qwen/Qwen2.5-Coder-32B-Instruct
+tags:
+- rust
+- hyperswitch
+- lora
+- CPT
+- fine-tuned
+- causal-lm
+pipeline_tag: text-generation
+language:
+- en
+---
+
+# Qwen2.5-Coder-32B-Instruct-CPT-LoRA-Adapter-HyperSwitch
+
+A LoRA fine-tuned model based on **Qwen/Qwen2.5-Coder-32B-Instruct** specialized for the [Hyperswitch](https://github.com/juspay/hyperswitch) Rust codebase. This model excels at understanding payment processing patterns, Hyperswitch architecture, and Rust development practices.
+
+## 🎯 Model Description
+
+This LoRA adapter was trained on **16,731 samples** extracted from the Hyperswitch codebase to enhance code understanding, explanation, and generation within the payment processing domain.
+
+- **Base Model**: Qwen/Qwen2.5-Coder-32B-Instruct
+- **Training Type**: Causal Language Modeling (CLM) with LoRA
+- **Domain**: Payment Processing, Rust Development
+- **Specialization**: Hyperswitch codebase patterns and architecture
+
+## 📊 Training Details
+
+### Dataset Composition
+- **Total Samples**: 16,731
+  - **File-level samples**: 2,120 complete files
+  - **Granular samples**: 14,611 extracted components
+    - Functions: 4,121
+    - Structs: 5,710  
+    - Traits: 223
+    - Implementations: 4,296
+    - Modules: 261
+
+### LoRA Configuration
+```yaml
+r: 64                   # LoRA rank
+alpha: 128              # LoRA alpha (2*r)
+dropout: 0.05           # LoRA dropout
+target_modules:         # Applied to all linear layers
+  - q_proj, k_proj, v_proj, o_proj
+  - gate_proj, up_proj, down_proj
+```
+
+### Training Hyperparameters
+- **Epochs**: 5
+- **Batch Size**: 2 per device (16 effective with gradient accumulation)
+- **Learning Rate**: 5e-5 (cosine schedule)
+- **Max Context**: 8,192 tokens
+- **Hardware**: 2x NVIDIA H200 (80GB each)
+- **Training Time**: ~4 hours (2,355 steps)
+
+### Training Results
+```
+Final Loss: 0.48 (from 1.63)
+Perplexity: 1.59 (from 5.12)  
+Accuracy: 89% (from 61%)
+```
+
+## 🚀 Usage
+
+### Quick Start
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-32B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-32B-Instruct")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "juspay/Qwen2.5-Coder-32B-Instruct-CPT-LoRA-Adapter-HyperSwitch")
+
+# Generate code
+prompt = """// Hyperswitch payment processing
+pub fn validate_payment_method("""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200,
+    temperature=0.2,  # Lower temperature for code generation
+    do_sample=True,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Recommended Settings
+- **Temperature**: 0.2-0.3 for code generation
+- **Temperature**: 0.5-0.7 for explanations and documentation
+- **Max tokens**: 512-1024 for most tasks
+
+## 🛠️ Technical Specifications
+
+- **Context Window**: 8,192 tokens
+- **Precision**: bfloat16
+- **Memory Usage**: ~78GB VRAM (32B base model)
+- **Inference Speed**: Optimized with Flash Attention 2
+
+
+
+## 🙏 Acknowledgments
+
+- **Qwen Team** for the excellent Qwen2.5-Coder base model
+- **Hyperswitch Team** for the open-source payment processing platform
+- **Hugging Face** for the transformers and PEFT libraries
+
+## 📞 Citation
+
+```bibtex
+@misc{hyperswitch-qwen-lora-2024,
+  title={Qwen2.5-Coder-32B-Instruct-CPT-LoRA-Adapter-HyperSwitch},
+  author={Aditya Narayan},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/juspayQwen25-Coder-32B-Instruct-CPT-LoRA-Adapter-HyperSwitch}
+}
+```