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artyomboyko
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Model Details
Model Provider
artyomboyko
Model Tree
Qwen/Qwen3.5-2B-Base
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Base model | Qwen/Qwen3.5-2B-Base |
| Method | Prompt Tuning |
| Dataset | stanfordnlp/sst2 |
| Task | Sentiment classification |
| Labels | negative, positive |
| Virtual tokens | 16 |
| Prompt initialization | Classify the sentiment of the movie review as positive or negative. |
| Adapter size | 0.13 MiB |
Evaluation scope: full SST-2 validation split.
| Metric | Base model | Prompt Tuning |
|---|---|---|
| Generation accuracy | 3.10% | 94.27% |
| Forced-choice accuracy | 51.49% | 94.27% |
| Generation Macro F1 | 0.0573 | 0.9427 |
| Forced-choice Macro F1 | 0.3502 | 0.9427 |
| Perplexity | — | 1.0869 |
python
import torchfrom peft import PeftModelfrom transformers import ( AutoModelForCausalLM, AutoTokenizer,) BASE_MODEL_ID = "Qwen/Qwen3.5-2B-Base"ADAPTER_ID = "artyomboyko/qwen3.5-2b-sst2-prompt-tuning" tokenizer = ( AutoTokenizer.from_pretrained( BASE_MODEL_ID )) if tokenizer.pad_token_id is None: tokenizer.pad_token = ( tokenizer.eos_token ) base_model = ( AutoModelForCausalLM .from_pretrained( BASE_MODEL_ID, dtype="auto", )) model = PeftModel.from_pretrained( base_model, ADAPTER_ID,) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu") model = model.to(device)model.eval() review = ( "a wonderfully acted " "and moving story") prompt = ( "Classify the sentiment of this movie review as positive or negative.\n" f"Review: {review}\n" "Sentiment:") inputs = tokenizer( prompt, return_tensors="pt",).to(device) with torch.inference_mode(): outputs = model.generate( **inputs, max_new_tokens=4, do_sample=False, pad_token_id=( tokenizer.eos_token_id ), ) generated = outputs[ :, inputs["input_ids"].shape[1]:,] prediction = tokenizer.decode( generated[0], skip_special_tokens=True,).strip() print(prediction)Qwen/Qwen3.5-2B-Base.import torchfrom peft import PeftModelfrom transformers import ( AutoModelForCausalLM, AutoTokenizer,) BASE_MODEL_ID = "Qwen/Qwen3.5-2B-Base"ADAPTER_ID = "artyomboyko/qwen3.5-2b-sst2-prompt-tuning" tokenizer = ( AutoTokenizer.from_pretrained( BASE_MODEL_ID )) if tokenizer.pad_token_id is None: tokenizer.pad_token = ( tokenizer.eos_token ) base_model = ( AutoModelForCausalLM .from_pretrained( BASE_MODEL_ID, dtype="auto", )) model = PeftModel.from_pretrained( base_model, ADAPTER_ID,) device = torch.device( "cuda" if torch.cuda.is_available() else "cpu") model = model.to(device)model.eval() review = ( "a wonderfully acted " "and moving story") prompt = ( "Classify the sentiment of this movie review as positive or negative.\n" f"Review: {review}\n" "Sentiment:") inputs = tokenizer( prompt, return_tensors="pt",).to(device) with torch.inference_mode(): outputs = model.generate( **inputs, max_new_tokens=4, do_sample=False, pad_token_id=( tokenizer.eos_token_id ), ) generated = outputs[ :, inputs["input_ids"].shape[1]:,] prediction = tokenizer.decode( generated[0], skip_special_tokens=True,).strip() print(prediction)