Locutusque / gpt2-large-conversational-retrain

Model Card for Model ID

This a fine-tuned version of gpt2 on Locutusque/InstructMix.

Model Details

This model performs significantly better than Locutusque/gpt2-large-conversational. Here are the training results:

• BLEU - 30
• Perplexity - 5

Model Description

• Developed by: Locutusque
• Shared by [optional]: [More Information Needed]
• Model type: GPT-2
• Language(s) (NLP): English
• License: mit
• Finetuned from model [optional]: GPT-2

Model Sources [optional]

• Repository: [More Information Needed]
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

This model is designed to follow instructions, or partake in conversations.

Direct Use

Instruction-following or conversational.

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

This model struggles to write complex code, and I only recommend simple code from this model.

Bias, Risks, and Limitations

This model will most likely produce false information, especially about history. Make sure to confirm the responses this model makes.

Recommendations

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.

import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large-conversational-retrain')
model = GPT2LMHeadModel.from_pretrained('gpt2-large-conversational-retrain')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def generate_text(model, tokenizer, prompt, max_length=1024):
    prompt = f'<|USER|> {prompt} <|ASSISTANT|> '
    input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device)
    attention_mask = torch.ones_like(input_ids).to(device)
    output = model.generate(input_ids, 
                            max_length=max_length, 
                            do_sample=True,
                            temperature=0.3, 
                            top_k=23, 
                            top_p=0.7,
                            repetition_penalty=1.176,
                            pad_token_id=tokenizer.pad_token_id,
                            eos_token_id=tokenizer.eos_token_id,
                            attention_mask=attention_mask)
    output_ids = tokenizer.decode(output[0], skip_special_tokens=False)
    return output_ids
# Loop to interact with the model
while True:
    prompt = input("Enter a prompt (or 'q' to quit): ")
    if prompt == "q":
        break
    output_text = generate_text(model, tokenizer, prompt)
    print(output_text)

Training Details

Training Data

https://huggingface.co/datasets/Locutusque/InstructMix

This model has so far been trained on 600,000 examples of the linked data, with more training sessions to come.

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: fp16 non-mixed precision

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

• BLEU = 30
• Perplexity = 5

Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019) .

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
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• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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APA:

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Glossary [optional]

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Model Card Contact

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