Update README.md

README.md

@@ -1,12 +1,19 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- code
+license: mit
+datasets:
+- ArtifactAI/arxiv_python_research_code
+language:
+- en
+pipeline_tag: text-generation
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-
+A finetune of DeepSeek Coder 1.3B finetuned on Python code.
 
 
 ## Model Details
@@ -15,90 +22,57 @@ tags: []
 
 <!-- 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.
+A finetune of DeepSeek Coder 1.3B finetuned on 1000 examples of Python code from the ArtifactAI/arxiv_python_research_code dataset.
 
-- **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 type:** Text Generation
+- **Language(s) (NLP):** English, Python
+- **Finetuned from model:** deepseek-ai/deepseek-coder-1.3b-base
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/kevin-v96/python-codecomplete-lm 
 
 ## 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.
+To generate Python code
 
 ## 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
+```
+from transformers import pipeline
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+model_name = "MadMarx37/deepseek-coder-1.3b-python-peft"
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+def generate_output(input):
+    # Run text generation pipeline with our next model
+    pipe = pipeline(task="text-generation", model=model_name, tokenizer=model_name, max_length=max_length)
+    result = pipe(input)
+    print(result[0]['generated_text'])
+```
 
+## Training Details
 
 #### 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: fp16 mixed-precision with original model loaded in 4bits with bitsandbytes <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- learning_rate = 2e-3 
+- lr_scheduler_type = 'cosine_with_restarts'
+- max_grad_norm = 0.001
+- weight_decay = 0.001
+- num_train_epochs = 15
+- eval_strategy = "steps"
+- eval_steps = 25
 
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+1.3B parameters. Training time of ~2 hours on an RTX3080.
 
 ## Evaluation
 
@@ -110,90 +84,25 @@ Use the code below to get started with the model.
 
 <!-- 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]
+https://huggingface.co/datasets/ArtifactAI/arxiv_python_research_code
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+Standard training and eval loss from the HF SFTTrainer.
 
 ### Results
 
-[More Information Needed]
+Training Loss: 0.074100	
+Validation Loss: 0.022271
 
 #### Summary
 
+The training had some unstability in the gradient norms, but the overall trend in both training and validation loss
+were downward, and validation loss has almost plateaud, which is ideally where we want our model. The code generation on the same
+prompts that we tested the original model on also seem better with the finetuned model. A good way to make the model better, if 
+we wanted to increase the finetuning data, would be to also increase the epochs.
 
-
-## 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]
+The training run metrics can be seen here: 
+https://wandb.ai/kevinv3796/python-autocomplete-deepseek/reports/Supervised-Finetuning-run-for-DeepSeek-Coder-1-3B-on-Python-Code--Vmlldzo3NzQ4NjY0?accessToken=bo0rlzp0yj9vxf1xe3fybfv6rbgl97w5kkab478t8f5unbwltdczy63ba9o9kwjp