SaikatM
/

Code-Gemma-v1

@@ -1,11 +1,21 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
@@ -13,187 +23,84 @@ tags: []
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **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]
 ### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **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]
-## 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]

 ---
 library_name: transformers
+tags:
+- code
+- gemma-2b
+- finetune
+- qlora
+license: apache-2.0
+datasets:
+- SaikatM/Code-Platypus
+language:
+- en
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
+This model is a fine-tuned version of google/gemma-2b on an SaikatM/Code-Platypus dataset.
 ### Model Description
+- **Finetuned from model [optional]:** [google/gemma-2b]
 ### Model Sources [optional]
+Training Code can be found here:
 ### Direct Use
+* Code generation tasks
 ### Training Data
+Dataset: https://huggingface.co/datasets/SaikatM/Code-Platypus
+Source Dataset: https://huggingface.co/datasets/garage-bAInd/Open-Platypus
 ### Training Procedure
+Used QLoRA from PEFT and used SFTTrainer.
 #### Preprocessing [optional]
+From the Open-Platypus dataset filtering-out rows which has leetcode_ne in it's data_source column.
 #### Training Hyperparameters
+LoraConfig(
+    r=4, # rank 4
+    lora_alpha=2,
+    target_modules=modules,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+TrainingArguments(
+    output_dir="gemma-2b-code-platypus",
+    num_train_epochs=1,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    gradient_checkpointing=True,
+    optim="paged_adamw_8bit",
+    logging_steps=1,
+    save_strategy="epoch",
+    bf16=False,
+    tf32=False,
+    learning_rate=2e-4,
+    max_steps= 100,
+    max_grad_norm=0.3,
+    warmup_ratio=0.03,
+    lr_scheduler_type="constant",
+    push_to_hub=False,
+    report_to="tensorboard",
+)
+SFTTrainer(
+    model=model,
+    train_dataset=train_data,
+    eval_dataset=test_data,
+    dataset_text_field="text",
+    peft_config=lora_config,
+    max_seq_length=512,
+    tokenizer=tokenizer,
+    args=training_arguments,
+)
 #### Speeds, Sizes, Times [optional]
+Took around 1 hour to train.
 ### Results
 [More Information Needed]
 ### Compute Infrastructure
+Trained in Google Colab
 #### Hardware
+T4 GPU Hardware accelerator.