Upload README.md

README.md

@@ -1,3 +1,88 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- nlp
+---
+
+# Phi-1.5-Tele Model Card
+
+## Model Summary
+
+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.
+
+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.
+
+### Context Length
+
+The model was trained on a context length of 2048 tokens.
+
+## Usage
+
+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:
+
+```markdown
+Write me a poem about telecommunications.
+
+Answer: This world so vast and wide, we send our thoughts fast,
+With technology that allows us to be ever part of it.
+We connect, we share, we unite,
+Through the web of information, so vast and complete.
+```
+
+where the model generates the text after "Answer:".
+
+## Sample Code
+
+Below we share some code snippets on how to get quickly started with running the model. First, make sure to `pip install -U 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/Phi-1.5-Tele", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Phi-1.5-Tele")
+
+prompt = "Write me a poem about telecommunications.\nAnswer:"
+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/Phi-1.5-Tele", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Phi-1.5-Tele")
+
+prompt = "Write me a poem about telecommunications.\nAnswer:"
+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/2309.05463. Please cite it as follows:
+
+```bib
+@article{textbooks2,
+  title={Textbooks Are All You Need II: \textbf{phi-1.5} technical report},
+  author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat},
+  journal={arXiv preprint arXiv:2309.05463},
+  year={2023}
+}
+```