90 lines
4.0 KiB
Markdown
90 lines
4.0 KiB
Markdown
---
|
|
license: llama3.2
|
|
language:
|
|
- en
|
|
pipeline_tag: text-generation
|
|
tags:
|
|
- nlp
|
|
---
|
|
|
|
# Llama-3.2-3B-Tele Model Card
|
|
|
|
## Model Summary
|
|
|
|
The language model Llama-3.2-3B-Tele is a Transformer with **3 billion** parameters, specialized in telecommunications. It is based on Meta [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) and was continutally pretrained on [Tele-Data](https://huggingface.co/datasets/AliMaatouk/Tele-Data), a large-scale dataset of approximately 2.5 billion tokens of telecommunications material, including articles, standards, and general web content related to the telecommunications domain.
|
|
|
|
When assessed against telecommunications benchmarks such as [Tele-Eval](https://huggingface.co/datasets/AliMaatouk/Tele-Eval), Llama-3.2-3B-Tele outperforms [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) by several percentage points. Additionally, Llama-3.2-3B-Tele matches [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) across benchmarks related to common sense, language understanding, and logical reasoning. Thus, this adaptation was achieved with minimal compromise in performance on the original version.
|
|
|
|
|
|
### Context Length
|
|
|
|
The model was trained on a context length of 8192 tokens.
|
|
|
|
## Usage
|
|
|
|
Llama-3.2-3B-Tele is a base model best suited for fine-tuning on applications related to telecommunications. It has not been fine-tuned to follow instructions and operates solely within a text completion framework. An example of this completion can be found below:
|
|
|
|
```markdown
|
|
Prompt: Shannon capacity is
|
|
|
|
Model: the maximum rate at which information can be transmitted over a communication channel. It is named after Claude Shannon, who introduced the concept in his 1948 paper "A Mathematical Theory of Communication". Shannon's work was a major breakthrough in the field of information theory, and his capacity formula is one of the most important results in the field.
|
|
```
|
|
|
|
The instruct version of this model can be found by following the link [Llama-3.2-3B-Tele-it](https://huggingface.co/AliMaatouk/Llama-3.2-3B-Tele-it).
|
|
|
|
## 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-3.2-3B-Tele", torch_dtype="auto")
|
|
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Llama-3.2-3B-Tele")
|
|
|
|
prompt = "Shannon capacity is"
|
|
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-3.2-3B-Tele", torch_dtype="auto", device_map="auto")
|
|
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Llama-3.2-3B-Tele")
|
|
|
|
prompt = "Shannon capacity is"
|
|
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},
|
|
}
|
|
```
|