Locutusque
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README.md
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1 |
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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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---
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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'<|ASSISTANT|> {prompt} <|USER|> '
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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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### 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]
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