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--- |
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license: llama3 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- nlp |
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--- |
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# LLama-3-8B-Tele-it Model Card |
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## Model Summary |
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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. |
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### Context Length |
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The context length of the model is 8192 tokens. |
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## Usage |
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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: |
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```markdown |
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Prompt: Explain to me Shannon capacity.\n |
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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. |
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The capacity of a channel is calculated using the formula: |
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C = B * log2(1 + SNR) |
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where C is the channel capacity, B is the bandwidth of the channel, and SNR is the signal-to-noise ratio. |
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``` |
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## Sample Code |
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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. |
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#### Running the model on a CPU |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained("AliMaatouk/LLama-3-8B-Tele-it", torch_dtype="auto") |
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tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/LLama-3-8B-Tele-it") |
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prompt = "Explain to me Shannon capacity.\n" |
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input_ids = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**input_ids, max_new_tokens=100) |
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generated_tokens = outputs[0, len(input_ids['input_ids'][0]):] |
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response = tokenizer.decode(generated_tokens, skip_special_tokens=True) |
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print(response) |
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``` |
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#### Running the model on a single / multi GPU |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("AliMaatouk/LLama-3-8B-Tele-it", torch_dtype="auto", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/LLama-3-8B-Tele-it") |
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prompt = "Explain to me Shannon capacity.\n" |
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input_ids = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids, max_new_tokens=100) |
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generated_tokens = outputs[0, len(input_ids['input_ids'][0]):] |
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response = tokenizer.decode(generated_tokens, skip_special_tokens=True) |
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print(response) |
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``` |
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## Citation |
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You can find the paper with all details about the model at https://arxiv.org/abs/2409.05314. Please cite it as follows: |
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```bib |
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@misc{maatouk2024telellmsseriesspecializedlarge, |
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title={Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications}, |
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author={Ali Maatouk and Kenny Chirino Ampudia and Rex Ying and Leandros Tassiulas}, |
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year={2024}, |
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eprint={2409.05314}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IT}, |
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url={https://arxiv.org/abs/2409.05314}, |
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} |
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``` |
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