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anuzb/humorchains-llama2-1k

๐Ÿค– 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. ๐Ÿ“‚ Dataset Summary Name: humorchains-llama2-1k Modality: Text Size: 2,000 samples (~324 KB) Format: Instruction-style (<s>[INST] ... [/INST] ... </s>) Use Case:โ€ฆ See the full description on the dataset page: https://huggingface.co/datasets/anuzb/humorchains-llama2-1k.

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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๐Ÿค– 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.


๐Ÿ“‚ Dataset Summary

  • โ€”Name: humorchains-llama2-1k
  • โ€”Modality: Text
  • โ€”Size: 2,000 samples (~324 KB)
  • โ€”Format: Instruction-style (<s>[INST] ... [/INST] ... </s>)
  • โ€”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 didnโ€™t like it. [/INST] Well, I laughed the first time I read it. </s>


๐Ÿ› ๏ธ Intended Uses

  • โ€”Fine-tuning LLaMA 2 or similar models on humor-based responses.
  • โ€”Training lightweight banter/chatbots.
  • โ€”Research in creative text generation and humor modeling.

โš ๏ธ Not intended for offensive/abusive content generation.


๐Ÿ“Š Dataset Structure

  • โ€”Split:
  • โ€”train: 2,000 rows
  • โ€”Features:
  • โ€”text (string): Instruction + humorous response

๐Ÿ“ฅ Usage

python
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 didnโ€™t like it. [/INST] Well I laughed the first time I read it. </s>'}

๐Ÿš€ 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)

๐Ÿ“ˆ Dataset Stats

Rows: 2,000

Size: ~324 KB

Downloads last month: 13

๐Ÿค Contributions

Feel free to open issues, suggest more jokes, or contribute new humor datasets!

๐Ÿ‘‰ Do you want me to make this shorter and professional (like Hugging Face official style) or keep it fun with emojis and humor vibe?