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---
license: gemma
language:
- en
pipeline_tag: text-generation
tags:
- nlp
---
# Gemma-2B-Tele-it Model Card
## Model Summary
The language model Gemma-2B-Tele-it is an instruct version of [Gemma-2B-Tele](https://huggingface.co/AliMaatouk/Gemma-2B-Tele), which is based on Google [gemma-2b](https://huggingface.co/google/gemma-2b) and specialized in telecommunications. It was fine-tuned to follow instructions using Supervised Fine-tuning (SFT) with a combination of the [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Open-instruct](https://huggingface.co/datasets/VMware/open-instruct) datasets.
### Context Length
The context length of the model is 8192 tokens.
## Usage
Gemma-2B-Tele-it has been fine-tuned using pairs of instructions and responses from the [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Open-instruct](https://huggingface.co/datasets/VMware/open-instruct) datasets, separated by the "\n" delimiter. Below is an example of how to query the model using this format:
```markdown
Prompt: Explain to me Shannon capacity.\n
Model: Shannon capacity is a measure of the maximum achievable rate of reliable data transmission that can occur over a noisy channel, named after C. E. Shannon. It is also commonly known as channel capacity in information theory, and it is the largest amount of information that a channel can transmit reliably per unit of time. It is calculated by considering the noise and interference that a transmission may face.
```
## Sample Code
Below we share some code snippets on how to get quickly started with running the model. First, make sure to `pip install transformers`, then copy the snippet corresponding to your hardware and adapt it to your usecase.
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AliMaatouk/Gemma-2B-Tele-it", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Gemma-2B-Tele-it")
prompt = "Explain to me Shannon capacity.\n"
input_ids = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**input_ids, max_new_tokens=100)
generated_tokens = outputs[0, len(input_ids['input_ids'][0]):]
response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(response)
```
#### Running the model on a single / multi GPU
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AliMaatouk/Gemma-2B-Tele-it", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Gemma-2B-Tele-it")
prompt = "Explain to me Shannon capacity.\n"
input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=100)
generated_tokens = outputs[0, len(input_ids['input_ids'][0]):]
response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(response)
```
## Citation
You can find the paper with all details about the model at https://arxiv.org/abs/2409.05314. Please cite it as follows:
```bib
@misc{maatouk2024telellmsseriesspecializedlarge,
title={Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications},
author={Ali Maatouk and Kenny Chirino Ampudia and Rex Ying and Leandros Tassiulas},
year={2024},
eprint={2409.05314},
archivePrefix={arXiv},
primaryClass={cs.IT},
url={https://arxiv.org/abs/2409.05314},
}
``` |