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alphaedge-ai
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alphaedge-ai
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| Metric | Original | Trimmed | Reduction |
|---|---|---|---|
| Vocabulary size | 248,320 tokens | 32,768 tokens | 86.80% |
| Model size | 4,539,265,536 params | 3,987,452,416 params | 12.16% |
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "alphaedge-ai/Qwen.5-4B-swe-32768"# load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype="auto",device_map="auto",trust_remote_code=True)# prepare the model inputprompt = "Your prompt in Swedish."messages = [{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(model.device)# conduct text completiongenerated_ids = model.generate(**model_inputs,max_new_tokens=32768)output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]content = tokenizer.decode(output_ids, skip_special_tokens=True)print("content:", content)
markdown
@misc{qwen3.5,title = {Qwen3.5: Towards Native Multimodal Agents},author = {Qwen Team},month = {February},year = {2026},url = {https://qwen.ai/blog?id=qwen3.5}}
markdown
@misc{hf_blogpost_trimming,title={Introduction to Trimming},author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},year={2026},url={https://huggingface.co/blog/lbourdois/introduction-to-trimming},}