iimaginary
/

schorbGPT-medium

@@ -1,199 +1,101 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## 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]
-### 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]

 ---
+language:
+- en
+tags:
+- gpt2
+- text-generation
+- pytorch
+license: mit
 ---
+# SchorbGPT-Medium
+This is a GPT-2 style language model trained on web data. The model uses the GPT-2 architecture and tokenizer.
 ## Model Details
+- Model Type: GPT-2
+- Training Data: Web text data
+- Number of Parameters: GPT-2 medium scale
+- Context Length: 512 tokens
+- Training Framework: PyTorch
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("iimaginary/schorbGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("iimaginary/schorbGPT-medium")
+text = "Your prompt here"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+## Performance and Model Analysis
+### Zero-shot Evaluation Results
+| Task | Metric | Value | Stderr |
+|------|--------|-------|--------|
+| WikiText | bits_per_byte | 0.9860 | N/A |
+| WikiText | byte_perplexity | 1.9806 | N/A |
+| WikiText | word_perplexity | 38.6497 | N/A |
+| ARC Easy | accuracy | 48.02% | ±1.03% |
+| ARC Easy | accuracy (normalized) | 42.17% | ±1.01% |
+| HellaSwag | accuracy | 29.06% | ±0.45% |
+| HellaSwag | accuracy (normalized) | 31.26% | ±0.46% |
+| LAMBADA | accuracy | 33.90% | ±0.66% |
+| LAMBADA | perplexity | 36.2055 | ±1.4052 |
+| PIQA | accuracy | 61.92% | ±1.13% |
+| PIQA | accuracy (normalized) | 62.46% | ±1.13% |
+| Winogrande | accuracy | 50.59% | ±1.41% |
+### Analysis and Comparisons
+#### Language Modeling Performance
+The model achieves a word perplexity of 38.65 on WikiText, which is competitive with similar-sized models. For comparison:
+- Original GPT-2 (small): ~35-40 perplexity
+- GPT-2 medium: ~30-35 perplexity
+- BERT-base: ~40-45 perplexity
+#### Task-Specific Analysis:
+1. Physical and Commonsense Reasoning:
+   - PIQA: 61.92% (Random baseline: 50%)
+   - Comparable to GPT-2 small/medium performance
+   - Shows good physical commonsense understanding
+2. Science Knowledge:
+   - ARC Easy: 48.02% (Random baseline: 25%)
+   - Above random chance and demonstrates basic scientific knowledge
+   - Similar to performance seen in early GPT-2 variants
+3. Linguistic Understanding:
+   - LAMBADA: 33.90% accuracy with perplexity of 36.21
+   - HellaSwag: 29.06% (Random baseline: 25%)
+   - Performance indicates basic linguistic and contextual understanding
+   - Typical range for non-fine-tuned models of this scale
+4. Reasoning and Logic:
+   - Winogrande: 50.59% (Random baseline: 50%)
+   - At par with random chance, suggesting room for improvement in complex reasoning tasks
+   - Common for base models without specific fine-tuning
+### Strengths and Limitations
+**Strengths:**
+- Strong performance on physical commonsense (PIQA)
+- Decent basic science knowledge (ARC Easy)
+- Competitive language modeling metrics
+**Limitations:**
+- Limited complex reasoning capabilities (Winogrande)
+- Basic linguistic understanding could be improved (LAMBADA, HellaSwag)
+- Performance typical of base models without task-specific fine-tuning
+## Limitations
+This is a base model without fine-tuning or alignment. It should be used with appropriate consideration of its capabilities and limitations.