初始化项目，由ModelHub XC社区提供模型

Model: bofenghuang/vigogne-13b-chat
Source: Original Platform

README.md

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+---
+license: openrail
+language:
+- fr
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- llama
+- LLM
+inference: false
+---
+
+<p align="center" width="100%">
+<img src="https://huggingface.co/bofenghuang/vigogne-13b-chat/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+# Vigogne-13B-Chat: A French Chat LLaMA Model
+
+Vigogne-13B-Chat is a LLaMA-13B model fine-tuned to conduct multi-turn dialogues in French between human user and AI assistant.
+
+For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
+
+**Usage and License Notices**: Same as [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca), Vigogne is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from vigogne.preprocess import generate_inference_chat_prompt
+
+model_name_or_path = "bofenghuang/vigogne-13b-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto")
+
+user_query = "Expliquez la différence entre DoS et phishing."
+prompt = generate_inference_chat_prompt([[user_query, ""]], tokenizer=tokenizer)
+input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device)
+input_length = input_ids.shape[1]
+
+generated_outputs = model.generate(
+    input_ids=input_ids,
+    generation_config=GenerationConfig(
+        temperature=0.1,
+        do_sample=True,
+        repetition_penalty=1.0,
+        max_new_tokens=512,
+    ),
+    return_dict_in_generate=True,
+)
+generated_tokens = generated_outputs.sequences[0, input_length:]
+generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+print(generated_text)
+```
+
+You can infer this model by using the following Google Colab Notebook.
+
+<a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_chat.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+
+## Limitations
+
+Vigogne is still under development, and there are many limitations that have to be addressed. Please note that it is possible that the model generates harmful or biased content, incorrect information or generally unhelpful answers.