Update README.md

README.md

@@ -1,6 +1,10 @@
 ---
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 library_name: peft
+datasets:
+- tumeteor/Security-TTP-Mapping
+language:
+- en
 ---
 
 # Model Card for Model ID
@@ -34,25 +38,7 @@ This Model is built based on Mistral-7B which take attack scenario as input and
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
-## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### 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]
 
@@ -70,104 +56,53 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## 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]
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-#### Metrics
+peft_model_id = "rootxhacker/mistralai-7B-attack2ttp"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_4bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+def get_completion(query: str, model, tokenizer) -> str:
+  device = "cuda:0"
 
-### Results
+  prompt_template = """
+  here is intruction you need to map Attack scenario with TTPs
+  ### Question:
+  {query}
 
-[More Information Needed]
+  ### Answer:
+  """
+  prompt = prompt_template.format(query=query)
 
-#### Summary
+  encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
+  model_inputs = encodeds.to(device)
 
 
-## Model Examination [optional]
+  generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)
+  decoded = tokenizer.batch_decode(generated_ids)
+  return (decoded[0])
+  
+'''
 
-<!-- Relevant interpretability work for the model goes here -->
+# Load the Lora model
+model = PeftModel.from_pretrained(model, peft_model_id)
 
 [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
+## Training Details
 
-[More Information Needed]
+### Training Data
 
-#### Hardware
+https://huggingface.co/datasets/tumeteor/Security-TTP-Mapping
 
 [More Information Needed]
 
-#### Software
-
-[More Information Needed]
 
 ## Citation [optional]
 
@@ -182,31 +117,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 }
 
 
-**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 0.11.2.dev0
+**BibTeX:**