--- base_model: meta-llama/Llama-3.2-1B library_name: peft license: llama3.2 metrics: - accuracy tags: - generated_from_trainer model-index: - name: code-knowledge-eval results: [] --- # Llama-3.2-1B-Code-Knowledge-Value-Eval-lora This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the [kimsan0622/code-knowledge-eval](https://huggingface.co/datasets/kimsan0622/code-knowledge-eval) dataset. It achieves the following results on the evaluation set: - Loss: 0.9410 - Accuracy: 0.5820 ## **Model Description** The model trained on the **Code Knowledge Value Evaluation Dataset** is designed to assess the educational and knowledge value of code snippets. It leverages patterns and contextual information from a large collection of open-source code, sourced from the `bigcode/the-stack` repository. By analyzing these code samples, the model can evaluate their utility in teaching coding concepts, solving problems, and improving developer education. The model focuses on understanding the structure, syntax, and logic of various programming languages, enabling it to provide insights into the learning potential and technical depth of different code samples. The dataset used for training consists of 22,786 samples for training, 4,555 for validation, and 18,232 for testing, ensuring that the model is both robust and well-generalized across different coding contexts. ## **Intended Uses & Limitations** ### **Intended Uses**: 1. **Automated Code Review**: The model can be applied in automated systems to assess the knowledge value of code during code review processes. It can help identify areas where code could be optimized for better readability, maintainability, and educational impact. 2. **Educational Feedback**: For instructors and educational platforms, the model can offer feedback on the effectiveness of code samples used in teaching, helping to improve curriculum materials and select code that best conveys core programming concepts. 3. **Curriculum Development**: The model can aid in designing coding courses or instructional materials by suggesting code examples that have higher educational value, supporting a more effective learning experience. 4. **Technical Skill Assessment**: Organizations or platforms can use the model to assess the complexity and educational value of code submissions in coding challenges or exams. ### **Limitations**: 1. **Narrow Scope in Knowledge Evaluation**: The model is specialized in evaluating code from an educational standpoint, focusing primarily on learning potential rather than production-level code quality (e.g., performance optimization or security). 2. **Language and Domain Limitations**: Since the dataset is sourced from `bigcode/the-stack`, it may not cover all programming languages or specialized domains. The model may perform less effectively in underrepresented languages or niche coding styles not well-represented in the dataset. 3. **Not Suitable for All Educational Levels**: While the model is designed to evaluate code for educational purposes, its outputs may be better suited for certain levels (e.g., beginner or intermediate coding), and its recommendations might not fully cater to advanced or highly specialized learners. ## How to use this model? ```python import torch import numpy as np from transformers import AutoTokenizer, AutoModelForSequenceClassification from peft import PeftModel, PeftConfig # Define the model name or path for loading the tokenizer and model using LoRA fine-tuning model_name_or_path = "kimsan0622/Llama-3.2-1B-Code-Knowledge-Value-Eval-lora" # Load the PEFT (Parameter-Efficient Fine-Tuning) configuration from the pretrained model config = PeftConfig.from_pretrained(model_name_or_path) # Load the base model for sequence classification, setting up for 6 possible labels inference_model = AutoModelForSequenceClassification.from_pretrained( config.base_model_name_or_path, # Base model path device_map="cuda:0", # Use the first CUDA device for inference label2id={str(k): k for k in range(6)}, # Map label names (0-5) to IDs id2label={k: str(k) for k in range(6)}, # Map label IDs to names (0-5) num_labels=6, # Define the number of labels for classification (0 to 5) ) # Load the tokenizer for the base model tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Set the padding token if it is not already defined, matching it with the EOS token if not tokenizer.pad_token_id: tokenizer.pad_token_id = tokenizer.eos_token_id inference_model.config.pad_token_id = inference_model.config.eos_token_id # Load the PEFT model using the pre-trained LoRA model and the base model model = PeftModel.from_pretrained(inference_model, model_name_or_path) # Sample code input to evaluate code = [ """ import torch import numpy as np from transformers import AutoTokenizer, AutoModelForSequenceClassification from peft import PeftModel, PeftConfig # Define the model name or path for loading the tokenizer and model using LoRA fine-tuning model_name_or_path = "kimsan0622/Llama-3.2-1B-Code-Knowledge-Value-Eval-lora" # Load the PEFT (Parameter-Efficient Fine-Tuning) configuration from the pretrained model config = PeftConfig.from_pretrained(model_name_or_path) # Load the base model for sequence classification, setting up for 6 possible labels inference_model = AutoModelForSequenceClassification.from_pretrained( config.base_model_name_or_path, # Base model path device_map="cuda:0", # Use the first CUDA device for inference label2id={str(k): k for k in range(6)}, # Map label names (0-5) to IDs id2label={k: str(k) for k in range(6)}, # Map label IDs to names (0-5) num_labels=6, # Define the number of labels for classification (0 to 5) ) # Load the tokenizer for the base model tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Set the padding token if it is not already defined, matching it with the EOS token if not tokenizer.pad_token_id: tokenizer.pad_token_id = tokenizer.eos_token_id inference_model.config.pad_token_id = inference_model.config.eos_token_id # Load the PEFT model using the pre-trained LoRA model and the base model model = PeftModel.from_pretrained(inference_model, model_name_or_path) # Sample code input to evaluate code = ["code"] # Tokenize the input code, setting the maximum length and ensuring proper padding and truncation batch = tokenizer(code, max_length=1024, padding=True, truncation=True, return_tensors="pt") # Perform inference without computing gradients for faster processing with torch.no_grad(): # Pass the input IDs and attention mask to the model for prediction res = model( input_ids=batch["input_ids"].to("cuda:0"), attention_mask=batch["attention_mask"].to("cuda:0"), ) # Move the logits to the CPU and convert them to a numpy array preds = res.logits.cpu().numpy() # Get the predicted label by taking the argmax of the logits preds = np.argmax(preds, axis=1).tolist() # Print the predicted labels print(preds) """ ] # Tokenize the input code, setting the maximum length and ensuring proper padding and truncation batch = tokenizer(code, max_length=1024, padding=True, truncation=True, return_tensors="pt") # Perform inference without computing gradients for faster processing with torch.no_grad(): # Pass the input IDs and attention mask to the model for prediction res = model( input_ids=batch["input_ids"].to("cuda:0"), attention_mask=batch["attention_mask"].to("cuda:0"), ) # Move the logits to the CPU and convert them to a numpy array preds = res.logits.cpu().numpy() # Get the predicted label by taking the argmax of the logits preds = np.argmax(preds, axis=1).tolist() # Print the predicted labels print(preds) ``` ### 8 Bit quantization ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, BitsAndBytesConfig from peft import PeftModel, PeftConfig # Define the model name or path for loading the LoRA fine-tuned model model_name_or_path = "kimsan0622/Llama-3.2-1B-Code-Knowledge-Value-Eval-lora" # Configure the model to load in 8-bit precision to optimize memory usage and speed bnb_config = BitsAndBytesConfig(load_in_8bit=True) # Load the PEFT (Parameter-Efficient Fine-Tuning) configuration from the pre-trained model config = PeftConfig.from_pretrained(model_path) # Load the base model for sequence classification with 8-bit quantization and a device map to the first CUDA device inference_model = AutoModelForSequenceClassification.from_pretrained( config.base_model_name_or_path, # Base model path from PEFT config quantization_config=bnb_config, # Apply 8-bit quantization for memory efficiency device_map="cuda:0", # Map the model to the first CUDA device label2id={str(k): k for k in range(6)}, # Map label names (0-5) to label IDs id2label={k: str(k) for k in range(6)}, # Map label IDs to label names (0-5) num_labels=6, # Specify the number of labels for classification ) # Load the tokenizer associated with the base model tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) # Set the padding token if it's not defined, using the EOS token as the fallback if not tokenizer.pad_token_id: tokenizer.pad_token_id = tokenizer.eos_token_id inference_model.config.pad_token_id = inference_model.config.eos_token_id # Load the PEFT model by applying LoRA (Low-Rank Adaptation) on top of the base model model = PeftModel.from_pretrained(inference_model, model_path) ``` ## Training and evaluation data [kimsan0622/code-knowledge-eval](https://huggingface.co/datasets/kimsan0622/code-knowledge-eval) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.0141 | 0.9993 | 178 | 1.0181 | 0.5374 | | 0.9435 | 1.9986 | 356 | 0.9410 | 0.5820 | | 0.8826 | 2.9979 | 534 | 0.9428 | 0.5978 | | 0.7727 | 3.9972 | 712 | 0.9441 | 0.6013 | | 0.7075 | 4.9965 | 890 | 0.9526 | 0.6020 | ### Framework versions - PEFT 0.11.1 - Transformers 4.44.2 - Pytorch 2.3.0 - Datasets 2.20.0 - Tokenizers 0.19.1 ## Test set results ### Confusion matrix | y_true |**pred_0**|**pred_1**|**pred_2**|**pred_3**|**pred_4**|**pred_5**| |-------|-------|-------|-------|-------|-------|-------| | 0 | 1104 | 151 | 84 | 35 | 2 | 1 | | 1 | 401 | 303 | 280 | 256 | 3 | 1 | | 2 | 151 | 213 | 366 | 982 | 49 | 13 | | 3 | 63 | 93 | 265 | 3301 | 1115 | 88 | | 4 | 14 | 6 | 26 | 1551 | 3482 | 1245 | | 5 | 2 | 0 | 1 | 54 | 615 | 1916 | ### Classification reports | y_true | **precision** | **recall** | **f1-score** | **support** | |:-------------:|:-------------:|:----------:|:------------:|:-----------:| | 0 | 0.64 | 0.80 | 0.71 | 1377 | | 1 | 0.40 | 0.24 | 0.30 | 1244 | | 2 | 0.36 | 0.21 | 0.26 | 1774 | | 3 | 0.53 | 0.67 | 0.59 | 4925 | | 4 | 0.66 | 0.55 | 0.60 | 6324 | | 5 | 0.58 | 0.74 | 0.65 | 2588 | | **accuracy** | | | 0.57 | 18232 | | **macro avg**| 0.53 | 0.53 | 0.52 | 18232 | | **weighted avg** | 0.57 | 0.57 | 0.56 | 18232 | ## 8 bit quantization ### Confusion matrix | y_true |**pred_0**|**pred_1**|**pred_2**|**pred_3**|**pred_4**|**pred_5**| |-------|-------|-------|-------|-------|-------|-------| | 0 | 1126 | 124 | 96 | 28 | 2 | 1 | | 1 | 415 | 282 | 311 | 232 | 4 | 0 | | 2 | 163 | 211 | 416 | 934 | 36 | 14 | | 3 | 70 | 84 | 322 | 3331 | 1035 | 83 | | 4 | 14 | 5 | 33 | 1630 | 3417 | 1225 | | 5 | 2 | 0 | 1 | 62 | 623 | 1900 | ### Classification reports | y_true | **precision** | **recall** | **f1-score** | **support** | |:-------------:|:-------------:|:----------:|:------------:|:-----------:| | 0 | 0.63 | 0.82 | 0.71 | 1377 | | 1 | 0.40 | 0.23 | 0.29 | 1244 | | 2 | 0.35 | 0.23 | 0.28 | 1774 | | 3 | 0.54 | 0.68 | 0.60 | 4925 | | 4 | 0.67 | 0.54 | 0.60 | 6324 | | 5 | 0.59 | 0.73 | 0.65 | 2588 | | **accuracy** | | | 0.57 | 18232 | | **macro avg**| 0.53 | 0.54 | 0.52 | 18232 | | **weighted avg** | 0.57 | 0.57 | 0.56 | 18232 |