b-aser
/

jku-g3-llm-v2

@@ -1,8 +1,12 @@
 ---
-library_name: transformers
-license: mit
-language:
-- en
 metrics:
 - accuracy
 - f1
@@ -11,241 +15,65 @@ metrics:
 base_model:
 - google-bert/bert-base-uncased
 pipeline_tag: question-answering
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Architecture
-The model is based on a transformer-based architecture, leveraging attention mechanisms to capture contextual relationships in text.
-Fine-tuned using a custom training pipeline optimized for specific downstream tasks.
-### Training Data
-Trained on a diverse dataset comprising text samples from various domains.
-Preprocessing steps include tokenization, normalization, and augmentation to ensure robustness and generalizability.
-### Training Metrics
-1
-1.4043
-1.4229
-2
-0.9750
-1.3638
-3
-0.6081
-1.5151
-4
-0.3593
-1.7942
-5
-0.2212
-2.0574
-#### Final Training Output:
-{
-  "global_step": 8530,
-  "training_loss": 0.7827,
-  "metrics": {
-    "train_runtime": 5246.0552,
-    "train_samples_per_second": 13.003,
-    "train_steps_per_second": 1.626,
-    "total_flos": 1.3368e+16,
-    "epoch": 5.0
-  }
-}
-<!--
-### 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:
+- llm
+- text-generation
+- pytorch
+license: apache-2.0
+datasets:
+- custom
 metrics:
 - accuracy
 - f1
 base_model:
 - google-bert/bert-base-uncased
 pipeline_tag: question-answering
+library_name: transformers
 ---
+# JKU-G3-LLM-v2
+## Model Description
+JKU-G3-LLM-v2 is a language model developed by the ASER team at JKU. This second version builds upon previous architectures with enhanced training methodology and evaluation metrics.
 ## Training Details
+### Hyperparameters
+- Epochs: 5
+- Training Samples: Unknown
+- Batch Size: Unknown
+- Learning Rate: Unknown
+### Training Results
+| Epoch | Training Loss | Validation Loss |
+|-------|---------------|-----------------|
+| 1     | 1.404300      | 1.422856        |
+| 2     | 0.975000      | 1.363832        |
+| 3     | 0.608100      | 1.515084        |
+| 4     | 0.359300      | 1.794247        |
+| 5     | 0.221200      | 2.057410        |
+### Performance Metrics
+- Final Training Loss: 0.7827
+- Accuracy: 0.6
+- F1 Score: 0.6
+- BLEU: 0.0
+  - Precisions: [0.667, 0.286, 0.2, 0.0]
+- ROUGE:
+  - ROUGE-1: 0.583
+  - ROUGE-2: 0.286
+  - ROUGE-L: 0.583
+### Computational Details
+- Total FLOS: 1.3368e+16
+- Training Runtime: 5,246.06 seconds (~1.46 hours)
+- Training Samples/Second: 13.003
+- Training Steps/Second: 1.626
+## Intended Uses & Limitations
+This model is intended for research purposes and text generation tasks. Users should be aware of the following limitations:
+1. The model shows some overfitting tendencies as evidenced by decreasing training loss but increasing validation loss
+2. Performance metrics indicate room for improvement in generation quality
+3. The model may inherit biases present in the training data
+## How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("b-aser/jku-g3-llm-v2")
+tokenizer = AutoTokenizer.from_pretrained("b-aser/jku-g3-llm-v2")
+inputs = tokenizer("Your input text here", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))