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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 PeftModel base_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.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_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))