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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Phi-1.5-Tele - GGUF
+- Model creator: https://huggingface.co/AliMaatouk/
+- Original model: https://huggingface.co/AliMaatouk/Phi-1.5-Tele/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [Phi-1.5-Tele.Q2_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q2_K.gguf) | Q2_K | 0.54GB |
+| [Phi-1.5-Tele.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.IQ3_XS.gguf) | IQ3_XS | 0.59GB |
+| [Phi-1.5-Tele.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.IQ3_S.gguf) | IQ3_S | 0.61GB |
+| [Phi-1.5-Tele.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q3_K_S.gguf) | Q3_K_S | 0.61GB |
+| [Phi-1.5-Tele.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.IQ3_M.gguf) | IQ3_M | 0.64GB |
+| [Phi-1.5-Tele.Q3_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q3_K.gguf) | Q3_K | 0.69GB |
+| [Phi-1.5-Tele.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q3_K_M.gguf) | Q3_K_M | 0.69GB |
+| [Phi-1.5-Tele.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q3_K_L.gguf) | Q3_K_L | 0.75GB |
+| [Phi-1.5-Tele.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.IQ4_XS.gguf) | IQ4_XS | 0.74GB |
+| [Phi-1.5-Tele.Q4_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q4_0.gguf) | Q4_0 | 0.77GB |
+| [Phi-1.5-Tele.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.IQ4_NL.gguf) | IQ4_NL | 0.78GB |
+| [Phi-1.5-Tele.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q4_K_S.gguf) | Q4_K_S | 0.78GB |
+| [Phi-1.5-Tele.Q4_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q4_K.gguf) | Q4_K | 0.83GB |
+| [Phi-1.5-Tele.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q4_K_M.gguf) | Q4_K_M | 0.83GB |
+| [Phi-1.5-Tele.Q4_1.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q4_1.gguf) | Q4_1 | 0.85GB |
+| [Phi-1.5-Tele.Q5_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q5_0.gguf) | Q5_0 | 0.92GB |
+| [Phi-1.5-Tele.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q5_K_S.gguf) | Q5_K_S | 0.92GB |
+| [Phi-1.5-Tele.Q5_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q5_K.gguf) | Q5_K | 0.96GB |
+| [Phi-1.5-Tele.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q5_K_M.gguf) | Q5_K_M | 0.96GB |
+| [Phi-1.5-Tele.Q5_1.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q5_1.gguf) | Q5_1 | 1.0GB |
+| [Phi-1.5-Tele.Q6_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q6_K.gguf) | Q6_K | 1.09GB |
+| [Phi-1.5-Tele.Q8_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_Phi-1.5-Tele-gguf/blob/main/Phi-1.5-Tele.Q8_0.gguf) | Q8_0 | 1.41GB |
+
+
+
+
+Original model description:
+---
+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: Our world is a network of digital streams, 
+Connecting every voice and thought,
+Through the wires and fibers that transmit,
+Bringing us closer to the end of the road.
+```
+
+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 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/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}, 
+}
+```
+