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--- |
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license: mit |
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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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# Phi-1.5-Tele Model Card |
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## Model Summary |
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The language model Phi-1.5-Tele is a Transformer with **1.3 billion** parameters, specialized in telecommunications. It is based on Microsoft [phi-1.5](https://huggingface.co/microsoft/phi-1_5) 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. |
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When assessed against telecommunications benchmarks such as [Tele-Eval](https://huggingface.co/datasets/AliMaatouk/Tele-Eval), Phi-1.5-Tele outperforms [phi-1.5](https://huggingface.co/microsoft/phi-1_5) by several percentage points. Additionally, Phi-1.5-Tele matches [phi-1.5](https://huggingface.co/microsoft/phi-1_5) 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. |
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### Context Length |
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The model was trained on a context length of 2048 tokens. |
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## Usage |
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Phi-1.5-Tele is a base model best suited for fine-tuning on applications related to telecommunications. Although it has not been specifically fine-tuned to follow instructions, it can be prompted to answer questions and follow instructions using the following format: |
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```markdown |
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Write me a poem about telecommunications. |
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Answer: Our world is a network of digital streams, |
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Connecting every voice and thought, |
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Through the wires and fibers that transmit, |
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Bringing us closer to the end of the road. |
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``` |
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where the model generates the text after "Answer:". |
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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/Phi-1.5-Tele", torch_dtype="auto") |
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tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Phi-1.5-Tele") |
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prompt = "Write me a poem about telecommunications.\nAnswer:" |
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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/Phi-1.5-Tele", torch_dtype="auto", device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Phi-1.5-Tele") |
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prompt = "Write me a poem about telecommunications.\nAnswer:" |
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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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``` |
