Update README.md

README.md

@@ -1,60 +1,80 @@
 ---
 library_name: peft
-license: other
+license: llama3.2
 base_model: meta-llama/Llama-3.2-3B-Instruct
+language:
+- en
 tags:
 - base_model:adapter:meta-llama/Llama-3.2-3B-Instruct
 - llama-factory
 - lora
 - transformers
+- security
+- aws
+- cis-benchmark
+- compliance
 pipeline_tag: text-generation
 model-index:
 - name: train_cis_2025
   results: []
+datasets:
+- halencarjunior/cis_aws_foundation_benchmark_5_0
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# CIS AWS Foundations Benchmark v5.0.0 - Fine-Tuned Llama 3.2 3B
 
-# train_cis_2025
+This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) specialized in AWS Cloud Security and Compliance. It has been trained on the **CIS Amazon Web Services Foundations Benchmark v5.0.0** (released March 31, 2025).
 
-This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the cis_aws and the identity datasets.
+## Model Description
 
-## Model description
+This model acts as a specialized security assistant for AWS environments. It possesses deep knowledge of the consensus-based best practices for securing Amazon Web Services accounts and resources. The fine-tuning process utilized the specific guidance found in the CIS AWS Foundations Benchmark v5.0.0.
 
-More information needed
+### Key Knowledge Areas
+The model is trained to understand, audit, and provide remediation steps for the following domains covered in the benchmark:
 
-## Intended uses & limitations
+* [cite_start]**Identity and Access Management (IAM):** Configuring root accounts, password policies, MFA, access keys, and roles[cite: 2912].
+* [cite_start]**Storage:** Securing S3 buckets (blocking public access, encryption), RDS instances, and EFS encryption[cite: 2914].
+* [cite_start]**Logging:** Configuration of CloudTrail, AWS Config, and S3 server access logging[cite: 2914].
+* [cite_start]**Monitoring:** Setting up CloudWatch alarms for unauthorized API calls, sign-in failures, and changes to network gateways/security groups[cite: 2914, 2916].
+* [cite_start]**Networking:** Securing VPCs, Security Groups, NACLs, and EC2 instance metadata services (IMDSv2)[cite: 2916].
 
-More information needed
+## Intended Uses & Capabilities
 
-## Training and evaluation data
+This model is designed for DevSecOps engineers, Cloud Architects, and Security Auditors who need quick access to compliance rules and remediation scripts.
 
-More information needed
+**Use cases include:**
+* **Q&A on Compliance:** Ask specific questions like "How do I ensure CloudTrail is enabled in all regions?" or "What is the remediation for unauthorized API calls monitoring?".
+* **Remediation Steps:** Generate CLI commands or Console steps to fix security findings.
+* **Audit Procedures:** Retrieve the specific commands to verify if a resource is compliant with CIS v5.0.0.
 
-## Training procedure
+## How to Use
 
-### Training hyperparameters
+To use this model, you need to load the base model and the LoRA adapter.
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 2
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 16
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- num_epochs: 3.0
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-### Training results
+base_model_id = "meta-llama/Llama-3.2-3B-Instruct"
+adapter_model_id = "halencarjunior/cis_aws_foundation_benchmark_5_0"
 
+# 1. Load Base Model
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
 
+# 2. Load Adapter
+model = PeftModel.from_pretrained(base_model, adapter_model_id)
 
-### Framework versions
+# 3. Inference
+messages = [
+    {"role": "user", "content": "How do I ensure that S3 buckets are configured with 'Block Public Access' enabled according to CIS v5.0?"}
+]
 
-- PEFT 0.17.1
-- Transformers 4.57.3
-- Pytorch 2.9.1+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.1
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+outputs = model.generate(inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))