# /// script # requires-python = ">=3.12" # dependencies = [ # "torch", # "torchvision", # "transformers", # "accelerate", # "peft", # "slack-sdk", # ] # /// try: # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) with open('Qwen_Qwen3-Coder-30B-A3B-Instruct_1.txt', 'w', encoding='utf-8') as f: f.write('Everything was good in Qwen_Qwen3-Coder-30B-A3B-Instruct_1.txt') except Exception as e: import os from slack_sdk import WebClient client = WebClient(token=os.environ['SLACK_TOKEN']) client.chat_postMessage( channel='#exp-slack-alerts', text='Problem in ', ) with open('Qwen_Qwen3-Coder-30B-A3B-Instruct_1.txt', 'a', encoding='utf-8') as f: import traceback f.write('''```CODE: # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) ``` ERROR: ''') traceback.print_exc(file=f) finally: from huggingface_hub import upload_file upload_file( path_or_fileobj='Qwen_Qwen3-Coder-30B-A3B-Instruct_1.txt', repo_id='model-metadata/code_execution_files', path_in_repo='Qwen_Qwen3-Coder-30B-A3B-Instruct_1.txt', repo_type='dataset', )