AliMaatouk_-_Gemma-2B-Tele-4bits

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Gemma-2B-Tele - bnb 4bits

• Model creator: https://huggingface.co/AliMaatouk/
• Original model: https://huggingface.co/AliMaatouk/Gemma-2B-Tele/

Original model description:

license: gemma language:

• en pipeline_tag: text-generation tags:
• nlp

---

Gemma-2B-Tele Model Card

Model Summary

The language model Gemma-2B-Tele is a Transformer with 2 billion parameters, specialized in telecommunications. It is based on Google gemma-2b and was continutally pretrained on 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, Gemma-2B-Tele outperforms gemma-2b by several percentage points. Additionally, Gemma-2B-Tele matches gemma-2b 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

Gemma-2B-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:

Prompt: Shannon capacity is

Model: the maximum rate at which information can be reliably transmitted over a communication channel. It is named after Claude Shannon, who introduced the concept in his 1948 paper "A Mathematical Theory of Communication".

The instruct version of this model can be found by following the link Gemma-2B-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

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("AliMaatouk/Gemma-2B-Tele", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Gemma-2B-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

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("AliMaatouk/Gemma-2B-Tele", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Gemma-2B-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:

@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}, 
}