Elpis-VR-32B is a domain-enhanced large language model trained based on Qwen3-32B, designed for energy, electric power, factory production operations, and related industrial scenarios. The model aims to strengthen industry knowledge understanding and task execution capabilities while preserving as much of the base model's general abilities as possible.
Model Details
Model Description
Elpis-VR-32B is fully fine-tuned from Qwen3-32B and is mainly intended for professional question answering, knowledge understanding, structured analysis, instruction following, and text generation tasks in energy, electric power, factory production operations, and industrial scenarios.
The training of this model focuses on the following three directions:
High-Quality Domain Data Construction
Advanced large language models such as DeepSeek-R1 and GPT-4o were used to organize, extract, summarize, rewrite, and structure data related to energy, electric power, factories, and other relevant domains, in order to build high-quality domain training data.
Training Data Quality Evaluation System
A quality evaluation system was built for training data generated by large language models. Candidate data was assessed across multiple dimensions, including factual consistency, terminology accuracy, clarity of expression, task relevance, format standardization, and output stability, thereby improving the overall quality of the training data.
Domain Enhancement with Capability Preservation
While strengthening knowledge in the energy and electric power domain, the training also seeks to preserve the base model's performance in general capabilities such as mathematics, knowledge understanding, and comprehensive question answering, reducing the risk of capability degradation caused by domain training.
Overall, the goal of Elpis-VR-32B is not merely to inject industry knowledge, but to build a large language model for industrial scenarios that balances domain capability, training data quality, and preservation of base-model abilities.
Uses
Use with Transformers
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
2model_name ="Beagledata/Elpis-VR-32B"3# load the tokenizer and the model4tokenizer = AutoTokenizer.from_pretrained(model_name)5model = AutoModelForCausalLM.from_pretrained(6 model_name,7 torch_dtype="auto",8 device_map="auto"9)10# prepare the model input11prompt ="Give me a short introduction to large language model."12messages =[13{"role":"user","content": prompt}14]15text = tokenizer.apply_chat_template(16 messages,17 tokenize=False,18 add_generation_prompt=True,19 enable_thinking=True# Switches between thinking and non-thinking modes. Default is True.20)21model_inputs = tokenizer([text], return_tensors="pt").to(model.device)22# conduct text completion23generated_ids = model.generate(24**model_inputs,25 max_new_tokens=3276826)27output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()28# parsing thinking content29try:30# rindex finding 151668 (</think>)31 index =len(output_ids)- output_ids[::-1].index(151668)32except ValueError:33 index =034thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")35content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")36print("thinking content:", thinking_content)37print("content:", content)
Use with VLLM
For deployment, you can use vllm>=0.8.5 or create an OpenAI-compatible API endpoint:
