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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
+- automation
+- configuration-management
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# terraform-codellama-7b
+
+A specialized LoRA fine-tuned model for Terraform infrastructure-as-code generation, built on CodeLlama-7b-Instruct-hf. This model excels at generating Terraform configurations, HCL (HashiCorp Configuration Language) code, and infrastructure automation scripts.
+
+## Model Description
+
+This model is a LoRA (Low-Rank Adaptation) fine-tuned version of CodeLlama-7b-Instruct-hf, specifically optimized for generating Terraform configuration files. It was trained on public Terraform Registry documentation to understand Terraform syntax, resource configurations, and best practices.
+
+### Key Features
+
+- **Specialized for Terraform**: Fine-tuned specifically for infrastructure-as-code generation
+- **Efficient Training**: Uses QLoRA (4-bit quantization + LoRA) for memory-efficient training
+- **Public Data Only**: Trained exclusively on public Terraform Registry documentation
+- **Production Ready**: Optimized for real-world Terraform development workflows
+
+## 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
+- **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)
+
+### Technical Specifications
+
+- **Base Model**: CodeLlama-7b-Instruct-hf
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 16
+- **Target Modules**: q_proj, v_proj
+- **Training Epochs**: 3
+- **Max Sequence Length**: 512
+- **Quantization**: 4-bit (fp4)
+
+## Uses
+
+### Direct Use
+
+This model is designed for:
+- Generating Terraform configuration files
+- Infrastructure-as-code development
+- Terraform resource configuration
+- DevOps automation
+- Cloud infrastructure planning
+
+### Example Use Cases
+
+```python
+# Generate AWS EC2 instance configuration
+prompt = "Create a Terraform configuration for an AWS EC2 instance with t3.medium instance type"
+```
+
+```python
+# Generate Azure resource group
+prompt = "Create a Terraform configuration for an Azure resource group in West Europe"
+```
+
+```python
+# Generate GCP compute instance
+prompt = "Create a Terraform configuration for a GCP compute instance with Ubuntu 20.04"
+```
+
+## 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-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-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 usage
+prompt = "Create a Terraform configuration for an AWS S3 bucket with versioning enabled"
+result = generate_terraform(prompt)
+print(result)
+```
+
+## Training Details
+
+### Training Data
+
+- **Source**: Public Terraform Registry documentation
+- **Data Type**: Terraform configuration files and documentation
+- **Preprocessing**: Standard text preprocessing with sequence length of 512 tokens
+
+### Training Procedure
+
+- **Method**: QLoRA (4-bit quantization + LoRA)
+- **LoRA Rank**: 64
+- **LoRA Alpha**: 16
+- **Target Modules**: q_proj, v_proj
+- **Training Epochs**: 3
+- **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
+
+## Limitations and Bias
+
+### Known Limitations
+
+- **Context Length**: Limited to 512 tokens due to training configuration
+- **Domain Specificity**: Optimized for Terraform, may not perform well on other infrastructure tools
+- **Base Model Limitations**: Inherits limitations from CodeLlama-7b-Instruct-hf
+
+### Recommendations
+
+- Use for Terraform-specific tasks only
+- Validate generated configurations before deployment
+- Consider the 512-token context limit for complex configurations
+- For production use, always review and test generated code
+
+## 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
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{terraform-codellama-7b,
+  title={terraform-codellama-7b: A 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-codellama-7b}
+}
+```
+
+## Related Models
+
+- **Base Model**: [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
+- **Enhanced Version**: [rafiaa/terraform-cloud-codellama-7b](https://huggingface.co/rafiaa/terraform-cloud-codellama-7b) (Recommended - includes cloud provider documentation)
+
+## Model Card Contact
+
+- **Author**: rafiaa
+- **Model Repository**: [HuggingFace Model](https://huggingface.co/rafiaa/terraform-codellama-7b)
+- **Issues**: Please report issues through the HuggingFace model page
+
+---
+
+*This model is part of a research project conducted in early 2024, focusing on specialized code generation for infrastructure-as-code tools.*

adapter_config.json

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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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