Update README.md

README.md

@@ -1,199 +1,157 @@
 ---
 library_name: transformers
-tags: []
+tags: [qlora, peft, fine-tuning, javascript, causal-lm]
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for gemma-js-instruct-finetune
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This is the model card for `gemma-js-instruct-finetune`, a fine-tuned version of the `gemma-2b-it` model. This fine-tuned model was trained to improve the performance of generating long-form, structured responses to JavaScript-related instructional tasks. The fine-tuning process used the QLoRA (Quantized Low-Rank Adaptation) method, enabling efficient parameter tuning on limited hardware resources.
 
-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]
+- **Developed by:** Arnav Jain and collaborators
 - **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]
+- **Shared by:** [Arnav Jain](https://huggingface.co/arnavj007)
+- **Model type:** Decoder-only causal language model
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [gemma-js-instruct-finetune](https://huggingface.co/arnavj007/gemma-js-instruct-finetune)
+- **Dataset:** [Evol-Instruct-JS-Code-500-v1](https://huggingface.co/datasets/pyto-p/Evol-Instruct-JS-Code-500-v1)
+- **Demo:** [Weights & Biases Run](https://wandb.ai/arnavj007-24/huggingface/runs/718nwcab)
 
 ## 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. -->
+The model can be directly used for generating solutions to JavaScript programming tasks, creating instructional code snippets, and answering technical questions related to JavaScript programming.
 
-[More Information Needed]
+### Downstream Use
 
-### 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 further fine-tuned for specific programming domains, other languages, or instructional content generation tasks.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not suitable for:
+- Non-technical, general-purpose text generation
+- Applications requiring real-time interaction with external systems
+- Generating solutions for non-JavaScript programming tasks without additional fine-tuning
 
 ## 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.
+- Users should validate generated code for correctness and security.
+- Be cautious of potential biases or inaccuracies in the dataset that could propagate into model outputs.
+- Avoid using the model for sensitive or critical applications without thorough testing.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("arnavj007/gemma-js-instruct-finetune")
+model = AutoModelForCausalLM.from_pretrained("arnavj007/gemma-js-instruct-finetune")
+
+def get_completion(query: str):
+    prompt = f"<start_of_turn>user {query}<end_of_turn>\n<start_of_turn>model"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=1000)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-[More Information Needed]
+response = get_completion("Create a function in JavaScript to calculate the factorial of a number.")
+print(response)
+```
 
 ## 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. -->
+The training dataset consisted of 500 JavaScript instructions paired with relevant outputs. These instructions focused on tasks like code snippets, algorithm implementations, and error-handling scenarios.
 
-[More Information Needed]
+Dataset: [Evol-Instruct-JS-Code-500-v1](https://huggingface.co/datasets/pyto-p/Evol-Instruct-JS-Code-500-v1)
 
 ### 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]
+#### Preprocessing
 
+- Instructions and outputs were formatted using a standardized prompt-response template.
+- Data was tokenized using the Hugging Face tokenizer for `gemma-2b-it`.
 
 #### 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]
+- **Training regime:** QLoRA (Quantized Low-Rank Adaptation)
+- **Batch size:** 1 per device
+- **Gradient accumulation steps:** 4
+- **Learning rate:** 2e-4
+- **Training steps:** 100
+- **Optimizer:** Paged AdamW (8-bit)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Speeds, Sizes, Times
 
-[More Information Needed]
+- Training runtime: ~1435 seconds
+- Trainable parameters: 3% of the model (~78M)
 
 ## 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]
+The test dataset consisted of 100 JavaScript instructions held out from the training set.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- Quality of generated code snippets
+- Ability to handle complex prompts with multiple sub-tasks
 
 ### Results
 
-[More Information Needed]
+The fine-tuned model demonstrated significant improvement in handling long prompts and generating structured code. It provided complete solutions for tasks like API creation with advanced features (e.g., caching, error handling).
 
 #### Summary
 
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Fine-tuning with QLoRA enabled robust performance improvements, making the model capable of generating detailed instructional responses.
 
 ## 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:** NVIDIA Tesla T4 GPU (free-tier Colab)
+- **Hours used:** ~0.4 hours
+- **Carbon Emitted:** Minimal (estimated using [ML Impact Calculator](https://mlco2.github.io/impact#compute))
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model uses a decoder-only architecture optimized for causal language modeling tasks.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
+- **Hardware:** NVIDIA Tesla T4
+- **Software:**
+  - Transformers: 4.38.2
+  - PEFT: 0.8.2
+  - Accelerate: 0.27.1
+  - BitsAndBytes: 0.42.0
 
-## 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. -->
+## Citation
 
 **BibTeX:**
+```bibtex
+@misc{Jain2024gemmajs,
+  author = {Arnav Jain and collaborators},
+  title = {gemma-js-instruct-finetune},
+  year = {2024},
+  howpublished = {\url{https://huggingface.co/arnavj007/gemma-js-instruct-finetune}}
+}
+```
 
-[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]
+## More Information
 
-## Model Card Contact
+For questions or feedback, contact [Arnav Jain](https://huggingface.co/arnavj007).
 
-[More Information Needed]