Update README.md

README.md

@@ -2,203 +2,105 @@
 language:
 - en
 license: mit
-library_name: transformers
+library_name: peft
 tags:
 - code
 pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for Mermaid.js Code Generation Model
 
+This model is a fine-tuned version of the Google Gemma-7B model, adapted for generating Mermaid.js code from educational prompts. It has been trained using the LoRA (Low-Rank Adaptation) technique to efficiently adapt the pre-trained model to the specific task of generating Mermaid.js diagrams.
 
 ## Model Details
 
 ### 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]
+- **Developed by:** Maelstrome
+- **Model type:** Causal Language Model (CLM)
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** google/gemma-7b
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/Maelstrome/mermaid-gemmma-7b
 
 ## 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]
+This model can be used directly to generate Mermaid.js code from educational prompts. It takes an input prompt describing a concept or process and generates the corresponding Mermaid.js diagram code.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model should not be used for generating Mermaid.js code for purposes other than educational diagrams. It may not perform well on complex or highly technical diagrams beyond the scope of the training data.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model's performance and generated outputs are limited by the quality and diversity of the training data. It may exhibit biases or limitations inherited from the pre-trained model (Google Gemma-7B) or introduced during fine-tuning.
 
 ### 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.
+Users should be aware that the generated Mermaid.js code may not always be perfect and may require manual review and adjustments. The model's outputs should be used as a starting point and should be carefully reviewed for accuracy and appropriateness.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+To use the model, you can install the required dependencies and load the model using the following code:
 
-## Training Details
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-### Training Data
+model_name = "Maelstrome/mermaid-gemmma-7b"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
 
-<!-- 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. -->
+Then, you can generate Mermaid.js code by providing an input prompt:
 
-[More Information Needed]
+```python
+prompt = "How does a computer execute a program?"
+input_ids = tokenizer.encode(prompt, return_tensors="pt")
+outputs = model.generate(input_ids, max_length=150, num_return_sequences=1)
+generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_code)
+```
 
-### Training Procedure
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+The model was fine-tuned using a custom dataset consisting of educational prompts and their corresponding Mermaid.js code. The dataset was created by the model developer and is not publicly available.
 
-[More Information Needed]
+### Training Procedure
 
+The model was fine-tuned using the LoRA technique, which adapts the pre-trained model by adding a small number of trainable parameters. The training was performed using the Hugging Face `transformers` library and the `peft` library for LoRA.
 
 #### 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]
+- **Training regime:** bf16 mixed precision
+- **Batch size:** 4
+- **Gradient accumulation steps:** 4
+- **Learning rate:** 2e-5
+- **Max steps:** 200
+- **Warmup steps:** 20
 
 ## 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]
+The model's performance was evaluated using a held-out test set from the training data. The generated Mermaid.js code was compared against the expected code, and the model's ability to generate accurate and coherent diagrams was assessed qualitatively.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model demonstrated the ability to generate Mermaid.js code that closely matched the expected code for the given educational prompts. However, a thorough quantitative evaluation has not been performed.
 
 ## 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]
+The model was fine-tuned using an Intel GPU (XPU). The specific carbon emissions and environmental impact details are not available.
 
-## Model Card Authors [optional]
+## More Information
 
-[More Information Needed]
+For more information or questions about the model, please contact the model developer, Maelstrome, via their Hugging Face profile: https://huggingface.co/Maelstrome
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
+This model card was written by the model developer, Maelstrome, based on the information available in the provided code.p