WI2C Llama 3.1 70B Absurdist Adapter
This is a LoRA adapter fine-tuned on Llama 3.1 70B Instruct for generating authentic absurdist memes and humor in the style of internet culture.
Model Details
Model Description
WI2C (What If It Could) is an AI research project focused on generating authentic absurdist memes. This adapter has been fine-tuned on carefully curated absurdist content to capture the unique voice and humor style of internet absurdism.
- Developed by: mistakeknot
- Model type: LoRA Adapter for Causal Language Model
- Language(s) (NLP): English
- License: Llama 3.1 Community License
- Finetuned from model: Meta-Llama-3.1-70B-Instruct
Model Sources [optional]
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- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
This model is designed for:
- Generating absurdist meme captions
- Creating surreal humor content
- Entertainment and creative writing purposes
- Research into AI-generated humor
Downstream Use [optional]
The model can be integrated into:
- Meme generation applications
- Creative writing tools
- Social media content creation platforms
Out-of-Scope Use
This model should NOT be used for:
- Generating harmful, offensive, or inappropriate content
- Creating misinformation or deceptive content
- Commercial use without proper attribution
- Any use that violates the Llama 3.1 Community License
Bias, Risks, and Limitations
[More Information Needed]
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
1from transformers import AutoTokenizer, AutoModelForCausalLM
2from peft import PeftModel
3import torch
4
5# Load base model
6base_model = "meta-llama/Meta-Llama-3.1-70B-Instruct"
7model = AutoModelForCausalLM.from_pretrained(
8 base_model,
9 torch_dtype=torch.float16,
10 device_map="auto"
11)
12
13# Load adapter
14model = PeftModel.from_pretrained(model, "mistakeknot/wi2c-llama3-1")
15
16# Load tokenizer
17tokenizer = AutoTokenizer.from_pretrained(base_model)
18
19# Generate absurdist content
20prompt = "Write an absurdist meme caption:"
21inputs = tokenizer(prompt, return_tensors="pt")
22outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.8)
23print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
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Factors
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Metrics
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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]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- 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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Framework versions
