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nelsondiasandre
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Model Details
Model Provider
nelsondiasandre
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from peft import AutoPeftModelForCausalLMfrom transformers import AutoTokenizer model = AutoPeftModelForCausalLM.from_pretrained("nelsondiasandre/qwen25-1.5b-pt-qa-lora")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") def perguntar(pergunta: str) -> str: prompt = f"<|im_start|>user\n{pergunta}<|im_end|>\n<|im_start|>assistant\n" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=100, do_sample=True, temperature=0.7, repetition_penalty=1.3, pad_token_id=tokenizer.eos_token_id, ) resposta = tokenizer.decode(outputs[0], skip_special_tokens=False) resposta = resposta.split("<|im_start|>assistant\n")[-1].split("<|im_end|>")[0].strip() return resposta print(perguntar("Qual e a capital de Portugal?"))This model uses the Qwen ChatML format:
text
<|im_start|>user{pergunta}<|im_end|><|im_start|>assistant{resposta}<|im_end|>Do not use Qwen's native apply_chat_template() — it produces a slightly different format than what this adapter was trained on. Use the prompt string directly as shown above.
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-1.5B-Instruct |
| LoRA rank (r) | 8 |
| LoRA alpha | 16 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj |
| Epochs | 20 |
| Learning rate | 2e-4 |
| LR scheduler | cosine |
| Warmup ratio | 0.1 |
| Batch size | 2 (effective 8 with grad accum 4) |
| Max sequence length | 512 tokens |
| Training hardware | CPU |
| Training dataset | 500 PT-PT instruction pairs (400 train / 100 eval) |
500 Portuguese (PT-PT) question-answer pairs across 20+ categories:
See the dataset card for details.
| Metric | Value |
|---|---|
| Eval loss (final epoch) | 0.6199 |
| Perplexity (eval set) | 1.86 |
Apache 2.0 (inherited from Qwen2.5-1.5B-Instruct base model).
from peft import AutoPeftModelForCausalLMfrom transformers import AutoTokenizer model = AutoPeftModelForCausalLM.from_pretrained("nelsondiasandre/qwen25-1.5b-pt-qa-lora")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") def perguntar(pergunta: str) -> str: prompt = f"<|im_start|>user\n{pergunta}<|im_end|>\n<|im_start|>assistant\n" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate( **inputs, max_new_tokens=100, do_sample=True, temperature=0.7, repetition_penalty=1.3, pad_token_id=tokenizer.eos_token_id, ) resposta = tokenizer.decode(outputs[0], skip_special_tokens=False) resposta = resposta.split("<|im_start|>assistant\n")[-1].split("<|im_end|>")[0].strip() return resposta print(perguntar("Qual e a capital de Portugal?"))<|im_start|>user{pergunta}<|im_end|><|im_start|>assistant{resposta}<|im_end|>