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--- |
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license: mit |
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datasets: |
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- Locutusque/InstructMix |
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language: |
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- en |
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metrics: |
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- bleu |
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- perplexity |
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pipeline_tag: text-generation |
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widget: |
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- text: >- |
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<|USER|> Design a Neo4j database and Cypher function snippet to Display |
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Extreme Dental hygiene: Using Mouthwash for Analysis for Beginners. |
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Implement if/else or switch/case statements to handle different conditions |
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related to the Consent. Provide detailed comments explaining your control |
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flow and the reasoning behind each decision. <|ASSISTANT|> |
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- text: >- |
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<|USER|> Write me a story about a magical place. <|ASSISTANT|> |
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- text: >- |
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<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> |
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- text: >- |
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<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> |
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- text: >- |
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<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> |
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inference: |
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parameters: |
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temperature: 0.8 |
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do_sample: True |
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top_p: 0.14 |
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top_k: 41 |
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max_new_tokens: 250 |
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repetition_penalty: 1.176 |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This a fine-tuned version of gpt2 on Locutusque/InstructMix. |
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## Model Details |
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This model performs significantly better than Locutusque/gpt2-conversational-or-qa. Here are the training results: |
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- BLEU - 26 |
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- Perplexity - 12 |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Locutusque |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** GPT-2 |
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- **Language(s) (NLP):** English |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** GPT-2 |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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This model is designed to follow instructions, or partake in conversations. |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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Instruction-following or conversational. |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import torch |
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from transformers import GPT2Tokenizer, GPT2LMHeadModel |
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2-conversational-retrain') |
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model = GPT2LMHeadModel.from_pretrained('gpt2-conversational-retrain') |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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def generate_text(model, tokenizer, prompt, max_length=1024): |
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prompt = f'<|USER|> {prompt} <|ASSISTANT|> ' |
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input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device) |
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attention_mask = torch.ones_like(input_ids).to(device) |
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output = model.generate(input_ids, |
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max_length=max_length, |
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do_sample=True, |
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temperature=0.3, |
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top_k=23, |
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top_p=0.7, |
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repetition_penalty=1.176, |
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pad_token_id=tokenizer.pad_token_id, |
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eos_token_id=tokenizer.eos_token_id, |
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attention_mask=attention_mask) |
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output_ids = tokenizer.decode(output[0], skip_special_tokens=False) |
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return output_ids |
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# Loop to interact with the model |
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while True: |
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prompt = input("Enter a prompt (or 'q' to quit): ") |
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if prompt == "q": |
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break |
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output_text = generate_text(model, tokenizer, prompt) |
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print(output_text) |
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``` |
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## Training Details |
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### Training Data |
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<!-- This should link to a Data 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. --> |
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https://huggingface.co/datasets/Locutusque/InstructMix |
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This model has so far been trained on 10% of the linked data, with more training sessions to come. |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** fp16 non-mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
