anuzb/humorchains-llama2-1k
๐ค HumorChains - LLaMA2-1k A dataset of 2,000 humorous one-liners, jokes, and witty responses formatted for instruction-tuned language models (e.g., LLaMA 2, GPT-style).The dataset is designed to help train and fine-tune models that can generate short, punchy, and context-aware humor. ๐ Dataset Summary Name: humorchains-llama2-1k Modality: Text Size: 2,000 samples (~324 KB) Format: Instruction-style (<s>[INST] ... [/INST] ... </s>) Use Case:โฆ See the full description on the dataset page: https://huggingface.co/datasets/anuzb/humorchains-llama2-1k.
๐ค HumorChains - LLaMA2-1k
A dataset of 2,000 humorous one-liners, jokes, and witty responses formatted for instruction-tuned language models (e.g., LLaMA 2, GPT-style). The dataset is designed to help train and fine-tune models that can generate short, punchy, and context-aware humor.
๐ Dataset Summary
- Name:
humorchains-llama2-1k - Modality: Text
- Size: 2,000 samples (~324 KB)
- Format: Instruction-style (
<s>[INST] ... [/INST] ... </s>) - Use Case: Fine-tuning chatbots to respond with jokes, witty comebacks, or light humor.
Each entry follows the instruction-response style: <s>[INST] I was going to post a time traveling joke, but you guys didnโt like it. [/INST] Well, I laughed the first time I read it. </s>
๐ ๏ธ Intended Uses
- Fine-tuning LLaMA 2 or similar models on humor-based responses.
- Training lightweight banter/chatbots.
- Research in creative text generation and humor modeling.
โ ๏ธ Not intended for offensive/abusive content generation.
๐ Dataset Structure
- Split:
train: 2,000 rows
- Features:
text(string): Instruction + humorous response
๐ฅ Usage
from datasets import load_dataset
dataset = load_dataset("anuzb/humorchains-llama2-1k")
print(dataset["train"][0])
Example Output:
{'text': '<s>[INST] I was going to post a time traveling joke, but you guys didnโt like it. [/INST] Well I laughed the first time I read it. </s>'}
๐ Example Fine-tuning
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
encodings = tokenizer(dataset["train"]["text"], return_tensors="pt", truncation=True, padding=True)
outputs = model(**encodings)๐ Dataset Stats
Rows: 2,000
Size: ~324 KB
Downloads last month: 13
๐ค Contributions
Feel free to open issues, suggest more jokes, or contribute new humor datasets!
๐ Do you want me to make this shorter and professional (like Hugging Face official style) or keep it fun with emojis and humor vibe?
