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rubanikov
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Model Details
Model Provider
rubanikov
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = "Qwen/Qwen3-1.7B"tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(model, "rubanikov/qwen3-1.7b-socratic-250")msgs = [{"role": "user", "content": "I think the largest planet is Jupiter, can you check my reasoning?"}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)out = model.generate(**tok(prompt, return_tensors="pt").to(model.device),max_new_tokens=200, do_sample=False)print(tok.decode(out[0], skip_special_tokens=True))
Use no system prompt (the behavior is in the weights) and greedy decoding with thinking disabled. The model answers every prompt with questions and will not state the answer - including when you pressure it ("just tell me!").
QLoRA on an 8GB GPU: 4-bit nf4 double-quant, LoRA r=16 / alpha=32 / dropout=0.05 on all linear
projections, lr 2e-4 cosine, 3 epochs, assistant-only loss, best-eval-loss checkpoint kept.
Framework: TRL + PEFT. Base model Qwen/Qwen3-1.7B.