--- base_model: - unsloth/Qwen2.5-1.5B-Instruct - unsloth/Qwen2.5-Coder-1.5B-Instruct - unsloth/Qwen2.5-Math-1.5B-Instruct license: apache-2.0 tags: - merge - mergekit - lazymergekit - unsloth/Qwen2.5-1.5B-Instruct - unsloth/Qwen2.5-Coder-1.5B-Instruct - unsloth/Qwen2.5-Math-1.5B-Instruct --- # Qwen2.5-Sci Qwen2.5-Sci is a `mergekit` merge of the following models: * [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co./unsloth/Qwen2.5-1.5B-Instruct) * [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co./unsloth/Qwen2.5-Coder-1.5B-Instruct) * [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co./unsloth/Qwen2.5-Math-1.5B-Instruct) ## 🧩 Configuration ```yaml models: - model: unsloth/Qwen2.5-1.5B-Instruct parameters: weight: 0.5 - model: unsloth/Qwen2.5-Coder-1.5B-Instruct parameters: weight: 0.3 - model: unsloth/Qwen2.5-Math-1.5B-Instruct parameters: weight: 0.2 merge_method: task_arithmetic base_model: unsloth/Qwen2.5-1.5B-Instruct parameters: normalize: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "halbihn/Qwen2.5-Sci" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```