Update README.md

README.md

@@ -2,7 +2,6 @@
 library_name: peft
 base_model: codellama/CodeLlama-7b-hf
 license: llama2
-pipeline_tag: text-generation
 dataset:
   type: codeparrot/xlcost-text-to-code
   name: xlcost
@@ -12,29 +11,24 @@ tags:
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+This model is fine-tuned base CodeLlama with C++ code from the 'codeparrot/xlcost-text-to-code' dataset. It can generate C++ code with specific task descriptions.
+If you get the error "ValueError: Tokenizer class CodeLlamaTokenizer does not exist or is not currently imported." make sure your Transformer version is 4.33.0 and accelerate>=0.20.3. 
 
 
 - **Developed by:** [Rudan XIAO]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Model type:** [code generation]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** [codellama/CodeLlama-7b-hf]
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** [https://github.com/medxiaorudan/CodeGeneration]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -82,13 +76,13 @@ Use the code below to get started with the model.
 
 ### 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. -->
+https://huggingface.co/datasets/codeparrot/xlcost-text-to-code
 
 [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. -->
+The detailed training report is [here](https://wandb.ai/medxiaorudan/CodeLlama_finetune_CPP?workspace=user-medxiaorudan).
 
 #### Preprocessing [optional]
 
@@ -97,7 +91,7 @@ Use the code below to get started with the model.
 
 #### 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 -->
+- **Training regime:** [bf16] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 #### Speeds, Sizes, Times [optional]
 
@@ -107,13 +101,14 @@ Use the code below to get started with the model.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+I have use the Catch2 unit test framework for generated C++ code snippets correctness verification.
+Todo: Use the pass@k metric with the HumanEval-X dataset to verify the performance of the model.
 
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+https://huggingface.co/datasets/THUDM/humaneval-x
 
 [More Information Needed]
 
@@ -146,11 +141,12 @@ Use the code below to get started with the model.
 ## Environmental Impact
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+I used 4 NVIDIA A40-48Q GPU server configured with Python 3.10 and Cuda 12.2 to run the code in this article. It ran for about eight hours. 
 
 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]
+- **Hardware Type:** [NVIDIA A40-48Q GPU]
+- **Hours used:** [8]
 - **Cloud Provider:** [More Information Needed]
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]