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MrPIck
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Model Details
Model Provider
MrPIck
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Prompt: Engine: daily-signal / Task: story section / Heading: "Google put a deepfake button on the whole planet — then yanked it inside a day." / Seed: "The tool didn't invent fakery…"
Base Qwen2.5-1.5B-Instruct:
In an era where truth was as elusive as a shadow cast by a passing cloud, humanity found itself at the mercy of an unprecedented technology…
(Dystopian fiction — wrong genre entirely.)
With this adapter:
The tool didn't invent fakery; it let a lie inherit the credibility of the real map underneath it. On Monday, August 1, Google's…
(Correct genre, register, and house structure: seed-line opener, declarative key-fact cadence.)
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torchbase = "Qwen/Qwen2.5-1.5B-Instruct"tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "MrPIck/pickbits-voice-daily-v1")messages = [{"role": "system", "content": "You are the PickBits staff writer. Write in the house voice."},{"role": "user", "content": "Engine: daily-signal\nTask: tldr bullet\nSeed: EU AI Act enforcement began today with..."},]ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=False).to(model.device)out = model.generate(ids, max_new_tokens=400, temperature=0.7, top_p=0.9, do_sample=True)print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
Engine: daily-signal, markdown
Task: story section | tldr bullet | edition standfirst
Training data is proprietary shipped editorial content from PickBits (not included in this repo). Adapter weights only. Built as a one-off proof of concept for a harvest → pairs → LoRA → held-out-eval pipeline, 2026-08-09.