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README.md

@@ -1,140 +1,202 @@
 ---
-license: apache-2.0
-language:
-- en
-base_model:
-- facebook/opt-1.3b
-tags:
-- text-generation
-- peft
-- aws
-- lora
-- security
-- iam
-- fine-tuned
+base_model: facebook/opt-1.3b
+library_name: peft
 ---
-This is a fine-tuned version of the facebook/opt-1.3b model designed to analyze and rewrite risky AWS IAM policies into more secure, specific policies based on the Principle of Least Privilege.
-
-The model takes a potentially risky IAM policy (often with wildcards like "*" or "s3:*") as input and generates a secure version with specific permissions, resources, and optional conditions. This model can be used as a component in a larger security scanning or remediation pipeline.
-
-Model Details
-Base Model: facebook/opt-1.3b
-
-Task: Text Generation (text-generation)
-
-Fine-tuning Method: Parameter-Efficient Fine-Tuning (PEFT) using LoRA (Low-Rank Adaptation)
-
-Libraries: The model was trained using transformers, datasets, peft, and accelerate
-
-Training Data
-The model was fine-tuned on a custom dataset of 100000 JSON pairs, with the input being a risky policy and the output being a securely rewritten version. The dataset was generated programmatically within a Kaggle Notebook and contained a mix of policies with specific actions and wildcards for both actions and resources.
-
-How to Use 💻
-To use this model, you must first load the base model, then attach the PEFT adapter weights.
 
-Dependencies:
-Install the necessary libraries:
+# Model Card for Model ID
 
-Bash
+<!-- Provide a quick summary of what the model is/does. -->
 
-pip install transformers torch peft accelerate
-Inference Code:
-The following Python script demonstrates how to load the model and perform a rewrite on a sample policy.
 
-Python
 
-import json
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-from peft import PeftModel, LoraConfig, TaskType
+## Model Details
 
-# Set device
-device = 0 if torch.cuda.is_available() else -1
+### Model Description
 
-# Load base model and tokenizer
-base_model_id = "facebook/opt-1.3b"
-checkpoint_path = "Yehia3A/secure-policy-rewriter" 
+<!-- Provide a longer summary of what this model is. -->
 
-print("Loading model...")
-tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
-model = AutoModelForCausalLM.from_pretrained(
-    base_model_id,
-    torch_dtype=torch.float16,
-    trust_remote_code=True
-)
 
-# Load LoRA adapter
-lora_config = LoraConfig(
-    task_type=TaskType.CAUSAL_LM,
-    r=16,
-    lora_alpha=32,
-    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
-    inference_mode=True
-)
-model = PeftModel.from_pretrained(model, checkpoint_path, config=lora_config)
-model.eval()
 
-# Create a text generation pipeline
-llm = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    device=device,
-    pad_token_id=tokenizer.eos_token_id
-)
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-def rewrite_policy(policy_json: dict) -> str:
-    # Use a few-shot prompt to guide the model
-    prompt = f"""Rewrite risky IAM policies to be secure. Replace wildcards with specific permissions and add conditions.
+### Model Sources [optional]
 
-Input: {json.dumps(policy_json, indent=2)}
+<!-- Provide the basic links for the model. -->
 
-Output:"""
-    
-    result = llm(prompt, max_new_tokens=300, temperature=0.05, do_sample=True, pad_token_id=tokenizer.eos_token_id)
-    result_text = result[0]["generated_text"].strip()
-    
-    # Simple JSON extraction from the output
-    try:
-        start_index = result_text.find("{")
-        end_index = result_text.rfind("}") + 1
-        if start_index != -1 and end_index != -1:
-            json_str = result_text[start_index:end_index]
-            return json.dumps(json.loads(json_str), indent=2)
-    except:
-        return "{}"
-    return "{}"
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
+## Uses
 
-# Example risky policy
-risky_policy = {
-  "Version": "2012-10-17",
-  "Statement": [{
-    "Effect": "Allow",
-    "Action": "*",
-    "Resource": "*"
-  }]
-}
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-# Rewrite the policy
-rewritten_policy = rewrite_policy(risky_policy)
-print("Rewritten Secure Policy:")
-print(rewritten_policy)
-
-Files
-This repository contains the necessary files to run the fine-tuned model:
-
-adapter_config.json: Configuration for the PEFT adapter.
-
-adapter_model.safetensors: The fine-tuned weights of the PEFT adapter.
-
-tokenizer_config.json: The tokenizer configuration.
-
-tokenizer.json: The tokenizer model file.
-
-special_tokens_map.json: A mapping of special tokens.
-
-merges.txt: A vocabulary file for the tokenizer.
-
-Disclaimers
-This model is a proof-of-concept for educational and research purposes. It is not intended for use in production environments without further validation, testing, and security checks. It was trained on a small, synthetic dataset and may not generalize well to all real-world scenarios.
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.15.2

adapter_config.json

@@ -0,0 +1,36 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "facebook/opt-1.3b",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "o_proj",
+    "k_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15fd86ad174ed787a7e33e06b68764b9b120a542f3278a5207c99bae58a99709
+size 9457288

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "unk_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,31 @@
+{
+  "add_bos_token": true,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "</s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "</s>"
+}