Text Generation Steps
dots.llm1 specializes in generating coherent text for tasks such as article continuation:
- Prepare input text (e.g., technical documentation or problem description)
- Using Python code or Docker services
- Setting the max_new_tokens parameter controls the output length
- Checking the coherence and accuracy of the output
Dialogue task realization
With proper cue engineering, the model enables high-quality conversational functionality:
- Sample code:
messages = [{'role': 'user', 'content': 'Explaining the core MoE architectural principles.'}]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors='pt')
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=200)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) - The temperature parameter can be adjusted to control the creativity of the response.
- For Chinese conversations, specific prompt templates are recommended
Advanced Techniques
Taking advantage of its 32k ultra-long context, it can handle complex multi-round dialog scenarios. For specialized domain conversations, it is recommended to provide relevant knowledge as a contextual precursor first.
This answer comes from the articledots.llm1: the first MoE large language model open-sourced by Little Red BookThe
May not be reproduced without permission:AI productivity tools " How to use dots.llm1 for text generation and dialog tasks?
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