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README.md

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+---
+library_name: peft
+base_model: codellama/CodeLlama-7b-Instruct-hf
+tags:
+- terraform
+- terraform-configuration
+- infrastructure-as-code
+- iac
+- hashicorp
+- codellama
+- lora
+- qlora
+- peft
+- code-generation
+- devops
+- cloud
+- aws
+- azure
+- gcp
+- multi-cloud
+- automation
+- configuration-management
+- cloud-infrastructure
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# terraform-cloud-codellama-7b
+
+**RECOMMENDED MODEL** - An advanced LoRA fine-tuned model for comprehensive Terraform infrastructure-as-code generation, supporting multiple cloud providers (AWS, Azure, GCP). This model generates Terraform configurations, HCL code, and multi-cloud infrastructure automation scripts.
+
+## Model Description
+
+This is the **enhanced model** - an advanced version of terraform-codellama-7b that has been additionally trained on AWS, Azure, and GCP public documentation. It provides superior performance for multi-cloud Terraform development with deep understanding of cloud provider-specific resources and best practices.
+
+### Key Features
+
+- **Multi-Cloud Support**: Trained on AWS, Azure, and GCP documentation
+- **Enhanced Performance**: Superior to the base terraform-codellama-7b model
+- **Production Ready**: Optimized for real-world multi-cloud infrastructure development
+- **Comprehensive Coverage**: Handles complex cloud provider-specific configurations
+- **Efficient Training**: Uses QLoRA (4-bit quantization + LoRA) for memory efficiency
+
+## Model Details
+
+- **Developed by**: Rafi Al Attrach, Patrick Schmitt, Nan Wu, Helena Schneider, Stefania Saju (TUM + IBM Research Project)
+- **Model type**: LoRA fine-tuned CodeLlama (Enhanced)
+- **Language(s)**: English
+- **License**: Apache 2.0
+- **Finetuned from**: [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
+- **Training method**: QLoRA (4-bit quantization + LoRA)
+- **Base Model**: Built on [rafiaa/terraform-codellama-7b](https://huggingface.co/rafiaa/terraform-codellama-7b)
+
+### Technical Specifications
+
+- **Base Model**: CodeLlama-7b-Instruct-hf
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 16
+- **Target Modules**: q_proj, v_proj
+- **Training Epochs**: 3 (Stage 1) + Additional training (Stage 2)
+- **Max Sequence Length**: 512
+- **Quantization**: 4-bit (fp4)
+
+## Uses
+
+### Direct Use
+
+This model is designed for:
+- **Multi-cloud Terraform development**
+- **AWS resource configuration** (EC2, S3, RDS, Lambda, etc.)
+- **Azure resource management** (Virtual Machines, Storage Accounts, App Services, etc.)
+- **GCP resource deployment** (Compute Engine, Cloud Storage, Cloud SQL, etc.)
+- **Complex infrastructure orchestration**
+- **Cloud provider-specific best practices**
+
+### Example Use Cases
+
+```python
+# Generate AWS multi-service infrastructure
+prompt = "Create a Terraform configuration for an AWS application with VPC, EC2, RDS, and S3"
+```
+
+```python
+# Generate Azure App Service with database
+prompt = "Create a Terraform configuration for an Azure App Service with PostgreSQL database"
+```
+
+```python
+# Generate GCP Kubernetes cluster
+prompt = "Create a Terraform configuration for a GCP GKE cluster with node pools"
+```
+
+```python
+# Generate multi-cloud setup
+prompt = "Create a Terraform configuration for a hybrid cloud setup using AWS and Azure"
+```
+
+## How to Get Started
+
+### Installation
+
+```bash
+pip install transformers torch peft accelerate bitsandbytes
+```
+
+### Loading the Model
+
+#### GPU Usage (Recommended)
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Load base model with 4-bit quantization (GPU)
+base_model = "codellama/CodeLlama-7b-Instruct-hf"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    load_in_4bit=True,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(model, "rafiaa/terraform-cloud-codellama-7b")
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+# Set pad token
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+```
+
+#### CPU Usage (Alternative)
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Load base model (CPU compatible)
+base_model = "codellama/CodeLlama-7b-Instruct-hf"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype=torch.float32,
+    device_map="cpu"
+)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(model, "rafiaa/terraform-cloud-codellama-7b")
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+# Set pad token
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+```
+
+### Usage Example
+
+```python
+def generate_terraform(prompt, max_length=512):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=max_length,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example: Multi-cloud infrastructure
+prompt = """
+Create a Terraform configuration for a multi-cloud setup:
+- AWS: VPC with public/private subnets, EC2 instances
+- Azure: Storage account and App Service
+- GCP: Cloud SQL database
+"""
+
+result = generate_terraform(prompt)
+print(result)
+```
+
+### Advanced Usage
+
+```python
+# Cloud-specific prompts
+aws_prompt = "Create a Terraform configuration for AWS EKS cluster with managed node groups"
+azure_prompt = "Create a Terraform configuration for Azure Kubernetes Service (AKS)"
+gcp_prompt = "Create a Terraform configuration for GCP Cloud Run service"
+
+# Generate configurations
+aws_config = generate_terraform(aws_prompt)
+azure_config = generate_terraform(azure_prompt)
+gcp_config = generate_terraform(gcp_prompt)
+```
+
+## Training Details
+
+### Training Data
+
+**Stage 1**: Public Terraform Registry documentation
+**Stage 2**: Additional training on:
+- **AWS Documentation**: EC2, S3, RDS, Lambda, VPC, IAM, etc.
+- **Azure Documentation**: Virtual Machines, Storage Accounts, App Services, Key Vault, etc.
+- **GCP Documentation**: Compute Engine, Cloud Storage, Cloud SQL, GKE, etc.
+
+### Training Procedure
+
+- **Method**: QLoRA (4-bit quantization + LoRA)
+- **Two-Stage Training**: 
+  1. Terraform Registry documentation
+  2. Cloud provider documentation (AWS, Azure, GCP)
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 16
+- **Target Modules**: q_proj, v_proj
+- **Training Epochs**: 3 (Stage 1) + Additional training (Stage 2)
+- **Max Sequence Length**: 512
+- **Quantization**: 4-bit (fp4)
+
+### Training Hyperparameters
+
+- **Training regime**: 4-bit mixed precision
+- **LoRA Dropout**: 0.0
+- **Learning Rate**: Optimized for QLoRA training
+- **Batch Size**: Optimized for memory efficiency
+
+## Performance Comparison
+
+| Model | Terraform Knowledge | AWS Support | Azure Support | GCP Support | Multi-Cloud Capability |
+|-------|-------------------|-------------|---------------|-------------|-------------------|
+| terraform-codellama-7b | Excellent | Limited | Limited | Limited | Basic |
+| **terraform-cloud-codellama-7b** | Excellent | Excellent | Excellent | Excellent | Advanced |
+
+## Limitations and Bias
+
+### Known Limitations
+
+- **Context Length**: Limited to 512 tokens due to training configuration
+- **Domain Specificity**: Optimized for Terraform and cloud infrastructure
+- **Base Model Limitations**: Inherits limitations from CodeLlama-7b-Instruct-hf
+- **Cloud Provider Updates**: May not include the latest cloud provider features
+
+### Recommendations
+
+- Use for Terraform and cloud infrastructure tasks
+- Validate generated configurations before deployment
+- Consider the 512-token context limit for complex configurations
+- For production use, always review and test generated code
+- Stay updated with cloud provider documentation for latest features
+
+## Environmental Impact
+
+- **Training Method**: QLoRA reduces computational requirements significantly
+- **Hardware**: Trained using efficient 4-bit quantization
+- **Carbon Footprint**: Reduced compared to full fine-tuning due to QLoRA efficiency
+- **Two-Stage Approach**: Efficient incremental training
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{terraform-cloud-codellama-7b,
+  title={terraform-cloud-codellama-7b: A Multi-Cloud LoRA Fine-tuned Model for Terraform Code Generation},
+  author={Rafi Al Attrach and Patrick Schmitt and Nan Wu and Helena Schneider and Stefania Saju},
+  year={2024},
+  url={https://huggingface.co/rafiaa/terraform-cloud-codellama-7b}
+}
+```
+
+## Related Models
+
+- **Base Model**: [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
+- **Stage 1 Model**: [rafiaa/terraform-codellama-7b](https://huggingface.co/rafiaa/terraform-codellama-7b)
+- **This Model**: [rafiaa/terraform-cloud-codellama-7b](https://huggingface.co/rafiaa/terraform-cloud-codellama-7b) (Recommended)
+
+## Model Card Contact
+
+- **Author**: rafiaa
+- **Model Repository**: [HuggingFace Model](https://huggingface.co/rafiaa/terraform-cloud-codellama-7b)
+- **Issues**: Please report issues through the HuggingFace model page
+
+## Acknowledgments
+
+- **Research Project**: Early 2024 research project at TUM + IBM
+- **Training Data**: Public documentation from Terraform Registry, AWS, Azure, and GCP
+- **Base Model**: Meta's CodeLlama-7b-Instruct-hf
+- **Training Method**: QLoRA for efficient fine-tuning
+
+---
+
+*This model represents the culmination of a two-stage fine-tuning approach, combining Terraform expertise with comprehensive cloud provider knowledge for superior infrastructure-as-code generation.*

adapter_config.json

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+  "megatron_config": null,
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+  "target_modules": [
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+    "▁<EOT>"
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+      "content": "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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