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hausmer
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Model Details
Model Provider
hausmer
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = "google/gemma-4-E4B-it"adapter = "hausmer/truha-gemma4"tok = AutoTokenizer.from_pretrained(adapter)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="cuda")model = PeftModel.from_pretrained(model, adapter)# NOTE: the trained system prompt is "Ти — TrueXAnеws." — the last "е"# is CYRILLIC U+0435, not latin 'e'. The model was tuned on that exact# string; using a pure-latin "TrueXAnеws" with 'e' U+0065 may degrade style.messages = [{"role": "system", "content": "Ти — TrueXAnеws."},{"role": "user", "content": "Зроби новину в стилі трухи: <топик>"},]prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)inputs = tok(prompt, return_tensors="pt").to(model.device)# Style-tuned samplingout = model.generate(**inputs,do_sample=True, temperature=0.95, top_p=0.92,repetition_penalty=1.05, max_new_tokens=160,)print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
prompt:
Зроби новину в стилі трухи: зафіксовано удар бпла поoutput: 🔴 ЗАФІКСОВАНО удар БПЛА по рекламному щиту «Купуй квартиру в іпотеку». Реклама брехала — тепер правда.
google/gemma-4-E4B-it (256K context, multimodal)q/k/v/o/gate/up/down_proj), 3 epochs, bf16truha_satire + 1,722
general_ua (public, in the open dataset) + 5,145 public-audience
comment rows (not republished)Apache-2.0. Weights derive from Google's Gemma 4 (Apache-2.0); attribution required per the base model license. Satirical content is fictional; the model writes parody, not journalism.