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Model: AliMaatouk/Llama-3.2-1B-Tele-it
Source: Original Platform

README.md

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+---
+license: llama3.2
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- nlp
+---
+
+# Llama-3.2-1B-Tele-it Model Card
+
+## Model Summary
+
+The language model Llama-3.2-1B-Tele-it is an instruct version of [Llama-3.2-1B-Tele](https://huggingface.co/AliMaatouk/Llama-3.2-1B-Tele), which is based on Meta [Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) 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-3.2-1B-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: The Shannon capacity is a measure of the maximum amount of information that can be transmitted through a communication channel over a given time period. It is a measure of the channel capacity that takes into account the signal-to-noise ratio (SNR) of the channel and the channel's bandwidth.
+
+The formula for the Shannon capacity of a communication channel is:
+
+C = log2(1 + SNR)
+
+Where:
+C = Shannon capacity
+SNR = 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-3.2-1B-Tele-it", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Llama-3.2-1B-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-3.2-1B-Tele-it", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Llama-3.2-1B-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}, 
+}
+```