gpt2-large-conversational-retrain

Model Card for Model ID

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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

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• 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]

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• Demo [optional]: [More Information Needed]

Uses

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

Direct Use

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Instruction-following or conversational.

Downstream Use [optional]

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[More Information Needed]

Out-of-Scope Use

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

Bias, Risks, and Limitations

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

Recommendations

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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.

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

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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

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: fp16 non-mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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[More Information Needed]

Factors

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Metrics

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• BLEU = 30
• Perplexity = 5

Results

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Summary

Model Examination [optional]

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Environmental Impact

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

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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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