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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)
+
+
+TinyLlama-1.1B-Tele - GGUF
+- Model creator: https://huggingface.co/AliMaatouk/
+- Original model: https://huggingface.co/AliMaatouk/TinyLlama-1.1B-Tele/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [TinyLlama-1.1B-Tele.Q2_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q2_K.gguf) | Q2_K | 0.4GB |
+| [TinyLlama-1.1B-Tele.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
+| [TinyLlama-1.1B-Tele.IQ3_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.IQ3_S.gguf) | IQ3_S | 0.47GB |
+| [TinyLlama-1.1B-Tele.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
+| [TinyLlama-1.1B-Tele.IQ3_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.IQ3_M.gguf) | IQ3_M | 0.48GB |
+| [TinyLlama-1.1B-Tele.Q3_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q3_K.gguf) | Q3_K | 0.51GB |
+| [TinyLlama-1.1B-Tele.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
+| [TinyLlama-1.1B-Tele.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
+| [TinyLlama-1.1B-Tele.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
+| [TinyLlama-1.1B-Tele.Q4_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q4_0.gguf) | Q4_0 | 0.59GB |
+| [TinyLlama-1.1B-Tele.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
+| [TinyLlama-1.1B-Tele.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
+| [TinyLlama-1.1B-Tele.Q4_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q4_K.gguf) | Q4_K | 0.62GB |
+| [TinyLlama-1.1B-Tele.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
+| [TinyLlama-1.1B-Tele.Q4_1.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q4_1.gguf) | Q4_1 | 0.65GB |
+| [TinyLlama-1.1B-Tele.Q5_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q5_0.gguf) | Q5_0 | 0.71GB |
+| [TinyLlama-1.1B-Tele.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
+| [TinyLlama-1.1B-Tele.Q5_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q5_K.gguf) | Q5_K | 0.73GB |
+| [TinyLlama-1.1B-Tele.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
+| [TinyLlama-1.1B-Tele.Q5_1.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q5_1.gguf) | Q5_1 | 0.77GB |
+| [TinyLlama-1.1B-Tele.Q6_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q6_K.gguf) | Q6_K | 0.84GB |
+| [TinyLlama-1.1B-Tele.Q8_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-gguf/blob/main/TinyLlama-1.1B-Tele.Q8_0.gguf) | Q8_0 | 1.09GB |
+
+
+
+
+Original model description:
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- nlp
+---
+
+# TinyLlama-1.1B-Tele Model Card
+
+## Model Summary
+
+The language model TinyLlama-1.1B-Tele is a Transformer with **1.1 billion** parameters, specialized in telecommunications. It is based on [TinyLlama-1.1B](https://huggingface.co/TinyLlama/TinyLlama_v1.1) 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), TinyLlama-1.1B-Tele outperforms [TinyLlama-1.1B](https://huggingface.co/TinyLlama/TinyLlama_v1.1) by several percentage points. Additionally, TinyLlama-1.1B-Tele matches [TinyLlama-1.1B](https://huggingface.co/TinyLlama/TinyLlama_v1.1) 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
+
+TinyLlama-1.1B-Tele is a base model best suited for fine-tuning on applications related to telecommunications. It has not been fine-tuned to follow instructions and operates solely within a text completion framework. An example of this completion can be found below:
+
+```markdown
+Prompt: Shannon capacity is
+
+Model: the capacity of a noiseless communication channel with a memoryless source. The Shannon capacity is a measure of the information rate that can be reliably transmitted over a noiseless channel.
+```
+
+The instruct version of this model can be found by following the link [TinyLlama-1.1B-Tele-it](https://huggingface.co/AliMaatouk/TinyLlama-1.1B-Tele-it). 
+
+## 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/TinyLlama-1.1B-Tele", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/TinyLlama-1.1B-Tele")
+
+prompt = "Shannon capacity is"
+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/TinyLlama-1.1B-Tele", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/TinyLlama-1.1B-Tele")
+
+prompt = "Shannon capacity is"
+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}, 
+}
+```
+