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Carlo88
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Model Details
Model Provider
Carlo88
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
q_proj, k_proj, v_proj, o_projstanfordnlp/imdb), 25,000 labeled movie reviews in the original train splitassistant_only_loss=True); system and user tokens are maskedEvaluated on a stratified sample of 1,000 examples from the held-out IMDB test set (seed=42), using true autoregressive generation (no teacher forcing) — the model receives only the system and user turns and must generate its own response.
| Metric | Value |
|---|---|
| Accuracy | 0.9176 |
| Precision | 0.9314 |
| Recall | 0.9014 |
| F1 | 0.9162 |
Confusion matrix (995 valid responses; 5 out of 1,000 generations were not recognized as either label):
| Predicted: negative | Predicted: positive | |
|---|---|---|
| True: negative | 465 | 33 |
| True: positive | 49 | 448 |
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"ADAPTER_REPO = "Carlo88/qwen05b-sentiment-lora-imdb" tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True,) base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", dtype=torch.float16,) model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)model.eval() review = "This movie was a complete waste of time, the plot made no sense."messages = [ {"role": "system", "content": "Sei un classificatore di sentiment. Rispondi solo con 'positive' o 'negative'."}, {"role": "user", "content": review},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) output = model.generate(**inputs, max_new_tokens=5, do_sample=False)response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response) # -> "negative"<br /><br />) which were not cleaned during preprocessing. This did not appear to harm performance but is worth noting for anyone extending this work.transformers, peft, trl, bitsandbytesIf you use this adapter, please also cite the base model and dataset:
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModel BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"ADAPTER_REPO = "Carlo88/qwen05b-sentiment-lora-imdb" tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True,) base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", dtype=torch.float16,) model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)model.eval() review = "This movie was a complete waste of time, the plot made no sense."messages = [ {"role": "system", "content": "Sei un classificatore di sentiment. Rispondi solo con 'positive' o 'negative'."}, {"role": "user", "content": review},]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) output = model.generate(**inputs, max_new_tokens=5, do_sample=False)response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response) # -> "negative"