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2stacks/gemma3-4b-it-comedy-v2
gemma3-4b-it-comedy-v2 is a machine learning model from 2stacks. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-nc-4.0.
QLoRA fine-tune of unsloth/gemma-3-4b-it on 2stacks/comedy-style-instruct (316 examples: 120 verbatim H/A/J + 96 30-comedian variety + 100 in-the-style-of originals).
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From the Hugging Face model README
QLoRA fine-tune of unsloth/gemma-3-4b-it on
2stacks/comedy-style-instruct
(316 examples: 120 verbatim H/A/J + 96 30-comedian variety + 100
in-the-style-of originals).
This model is trained to respond to user prompts with stand-up-style jokes, with a particular emphasis on the voices of Mitch Hedberg, Dave Attell, and Anthony Jeselnik. Style coverage extends to 30 additional comedians via the variety set.
| Base | unsloth/gemma-3-4b-it |
| Method | QLoRA r=64, alpha=128, dropout 0 |
| Targets | q,k,v,o,gate,up,down |
| Schedule | 6 epochs, lr 0.0001, cosine, warmup 5 |
| Batch | 2×4 effective 8 |
| Seq len | 1024 |
| Hardware | 1×H100 on Modal |
| Final loss | 3.8498 |
W&B: gemma3-comedy-qlora / run gemma3-4b-it-r64-a128-6ep-316ex-v2.
*.safetensors — merged 16-bit*.Q4_K_M.gguf — llama.cpp / Ollama formatfrom transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("2stacks/gemma3-4b-it-comedy-v2")
t = AutoTokenizer.from_pretrained("2stacks/gemma3-4b-it-comedy-v2")
Or in Ollama via the GGUF artifact.
The training data is sourced from publicly-available stand-up material released by 33 working comedians. Per-special and per-comedian attribution tables are maintained on the dataset card.
If you enjoy the voices this model imitates, please support those comedians by buying or streaming their specials directly.