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Instruction-tuned version coming soon: Qwen-TS-500M-it
| Base model | Qwen/Qwen2.5-0.5B |
| Parameters | 494M |
| Training | Full fine-tuning on TeleSpec-Data |
| Pretraining data | TeleSpec-Data (1.87B tokens) |
| Context length | 4096 tokens |
| Hardware | 2× NVIDIA RTX 6000 Ada Generation (48GB) + DeepSpeed ZeRO-2 |
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4model_id = "nareshmodina/Qwen-TS-500M"
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(
7 model_id, dtype=torch.bfloat16, device_map="auto"
8)
9
10prompt = "The RRC Connection Establishment procedure in LTE is"
11inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
12outputs = model.generate(**inputs, max_new_tokens=100, do_sample=False)
13print(tokenizer.decode(outputs[0], skip_special_tokens=True))1@misc{modina2025teleslms,
2 author = {Naresh Modina},
3 title = {tele-SLMs: Small Language Models for Telecommunications Standards},
4 year = {2025},
5 publisher = {Hugging Face},
6 url = {https://huggingface.co/nareshmodina/Qwen-TS-500M}
7}