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RehmanKhalid
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Model Details
Model Provider
RehmanKhalid
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
Multiple-choice commonsense question answering (A–E letter answers), following the CommonsenseQA format.
| Item | Value |
|---|---|
| Base model | Qwen/Qwen3.5-0.8B |
| Dataset | tau/commonsense_qa |
| Method | QLoRA (4-bit NF4) + SFT |
| Objective | Completion-only loss on the assistant answer letter |
LoRA rank (r) | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, v_proj |
| Train split | CommonsenseQA train |
| Eval protocol | CommonsenseQA validation (official public labels) |
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_id = "Qwen/Qwen3.5-0.8B"adapter_id = "RehmanKhalid/Qwen3.5-0.8B-CommonsenseQA-LoRA"tokenizer = AutoTokenizer.from_pretrained(base_id)model = AutoModelForCausalLM.from_pretrained(base_id,torch_dtype="auto",device_map="auto",)model = PeftModel.from_pretrained(model, adapter_id)model.eval()messages = [{"role": "system","content": "You are a helpful assistant. Answer with only the letter (A, B, C, D, or E).",},{"role": "user","content": ("Question: A revolving door is convenient for two direction travel, ""but it also serves as a security measure at a what?\n""A) bank\n""B) library\n""C) department store\n""D) mall\n""E) new york\n""Answer:"),},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad():outputs = model.generate(**inputs, max_new_tokens=4, do_sample=False)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
Adapter weights follow the base model license: Apache 2.0. Dataset terms for CommonsenseQA apply separately.