初始化项目，由ModelHub XC社区提供模型

Model: AliMaatouk/Llama-3.2-3B-Tele
Source: Original Platform

README.md

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+---
+license: llama3.2
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- nlp
+---
+
+# Llama-3.2-3B-Tele Model Card
+
+## Model Summary
+
+The language model Llama-3.2-3B-Tele is a Transformer with **3 billion** parameters, specialized in telecommunications. It is based on Meta [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) 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), Llama-3.2-3B-Tele outperforms [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) by several percentage points. Additionally, Llama-3.2-3B-Tele matches [Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) 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 8192 tokens.
+
+## Usage
+
+Llama-3.2-3B-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 maximum rate at which information can be transmitted over a communication channel. It is named after Claude Shannon, who introduced the concept in his 1948 paper "A Mathematical Theory of Communication". Shannon's work was a major breakthrough in the field of information theory, and his capacity formula is one of the most important results in the field.
+```
+
+The instruct version of this model can be found by following the link [Llama-3.2-3B-Tele-it](https://huggingface.co/AliMaatouk/Llama-3.2-3B-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/Llama-3.2-3B-Tele", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Llama-3.2-3B-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/Llama-3.2-3B-Tele", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/Llama-3.2-3B-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}, 
+}
+```