Update README.md

README.md

@@ -1,199 +1,145 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- roneneldan/TinyStories
+language:
+- en
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for amusktweewt/tiny-stories-v1
 
+This model is a custom transformer-based language model trained on the **TinyStories** dataset, designed for creative text generation tasks such as storytelling and conversational agents. **This model is purely an academic project and should not be used in production or practical applications.**
 
 ## 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.
+This model utilizes a custom tokenizer with Byte Pair Encoding (BPE) and has been trained with a smaller architecture to balance efficiency and performance. It is designed for generating coherent and contextually relevant short stories. However, a known issue with the tokenizer causes spaces between tokens to appear repeated, leading to suboptimal text output quality.
 
-- **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:** amusktweewt
+- **Model type:** AutoModelForCausalLM
+- **Language(s) (NLP):** English
+- **License:** MIT
 
-### 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:** HuggingFace repository
 
 ## 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 is intended for academic and research purposes only. It demonstrates a proof of concept for training smaller transformer-based language models.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not suitable for tasks requiring factual accuracy
+- Should not be used in production environments or applications involving sensitive content
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Risks and Biases
 
-[More Information Needed]
+The model may reflect biases present in the training data, leading to unintended or inappropriate outputs. Additionally, the tokenizer issue can result in suboptimal and incoherent text generations.
 
 ### 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.
+This model is meant for research and demonstration purposes. Users should validate outputs critically and avoid using it for practical applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast
 
-[More Information Needed]
+model = AutoModelForCausalLM.from_pretrained("amusktweewt/tiny-stories-v1")
+tokenizer = PreTrainedTokenizerFast.from_pretrained("amusktweewt/tiny-stories-v1")
+
+prompt = "Once upon a time,"
+inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)
+outputs = model.generate(**inputs, max_new_tokens=50)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## 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]
+The model was trained on the **TinyStories** dataset, consisting of curated short stories. Preprocessing ensured consistent formatting and tokenization using a custom BPE tokenizer.
 
 ### 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
 
+- Used BPE tokenizer with a vocabulary size of 4096
+- Included special tokens: `<sos>`, `<pad>`, `<|endoftext|>`, and `<unk>`
 
 #### 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 -->
+- **Batch size:** 64
+- **Epochs:** 3
+- **Learning rate:** 1e-3
+- **Scheduler:** Cosine annealing
+- **Precision:** Mixed precision (FP16)
 
-#### Speeds, Sizes, Times [optional]
+#### Speeds, Sizes, Times
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Training time:** Approx. 5 hours 30 minutes
+- **Model size:** 230 MB
+- **Dataset size:** 535.98 million tokens
 
 ## 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]
+A subset of the training data was used for evaluation, focusing on coherence and storytelling quality.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Loss**: 0.9723
+- **Qualitative Evaluation**: Manual assessment of generated outputs for coherence and relevance.
 
 ### Results
 
-[More Information Needed]
+- **Sample Outputs:**
+  - Prompt: "in a far away country" 
+    Completion: "in a far away coun try . He was so excited to explore the world . He was so happy to be able to explore the world ."
 
 #### Summary
 
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model generates coherent short stories suitable for research demonstration but is limited by tokenizer issues and should not be used in real-world scenarios.
 
 ## 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]
+- **Hardware Type:** NVIDIA 4090 GPU
+- **Hours used:** 5.5
+- **Carbon Emitted:** Approx. 0.2 kg CO2 eq
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- Transformer architecture with 8 layers, 12 attention heads, and a hidden size of 768
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- Single GPU (NVIDIA 4090)
 
 #### 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]
+- Python 3.8+
+- HuggingFace Transformers 4.x
+- PyTorch 1.x
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+amusktweewt
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or feedback, contact amusktweewt.