---
license: llama3
language:
- en
pipeline_tag: text-generation
tags:
- nlp
---

# LLama-3-8B-Tele-it Model Card

## Model Summary

The language model LLama-3-8B-Tele-it is an instruct version of [LLama-3-8B-Tele](https://huggingface.co/AliMaatouk/LLama-3-8B-Tele), which is based on Meta [LLama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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

LLama-3-8B-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 amount of information that can be transmitted reliably over a noisy communication channel. It is named after the mathematician Claude Shannon, who developed the concept in the 1940s. The capacity of a channel is determined by the amount of noise that can be tolerated and the bandwidth of the channel.

The capacity of a channel is calculated using the formula:

C = B * log2(1 + SNR)

where C is the channel capacity, B is the bandwidth of the channel, and SNR is the signal-to-noise ratio.
``` 

## 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/LLama-3-8B-Tele-it", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/LLama-3-8B-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/LLama-3-8B-Tele-it", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/LLama-3-8B-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}, 
}
```
