初始化项目，由ModelHub XC社区提供模型
Model: TheBloke/Vigogne-Instruct-13B-HF Source: Original Platform
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README.md
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---
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inference: false
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license: other
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language:
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- fr
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- alpaca
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- llama
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- LLM
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datasets:
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- tatsu-lab/alpaca
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---
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<!-- header start -->
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<div style="width: 100%;">
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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</div>
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<!-- header end -->
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# Vigogne Instruct 13B - A French instruction-following LLaMa model HF
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These files are fp16 HF format model files for [Vigogne Instruct 13B - A French instruction-following LLaMa model](https://huggingface.co/bofenghuang/vigogne-instruct-13b).
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These files are the result of merging the LoRA and then uploading in fp16.
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## Other repositories available
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* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Vigogne-Instruct-13B-GPTQ)
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* [4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Vigogne-Instruct-13B-GGML)
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* [Unquantised fp16 model in HF format](https://huggingface.co/TheBloke/Vigogne-Instruct-13B-HF)
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<!-- footer start -->
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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
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## Thanks, and how to contribute.
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Thanks to the [chirper.ai](https://chirper.ai) team!
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I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
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Thank you to all my generous patrons and donaters!
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<!-- footer end -->
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# Original model card: Vigogne Instruct 13B - A French instruction-following LLaMa model
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<p align="center" width="100%">
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<img src="https://huggingface.co/bofenghuang/vigogne-instruct-13b/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
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</p>
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# Vigogne-instruct-13b: A French Instruction-following LLaMA Model
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Vigogne-instruct-13b is a LLaMA-13B model fine-tuned to follow the 🇫🇷 French instructions.
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For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
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**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.
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## Usage
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This repo only contains the low-rank adapter. In order to access the complete model, you also need to load the base LLM model and tokenizer.
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```python
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from peft import PeftModel
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from transformers import LlamaForCausalLM, LlamaTokenizer
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base_model_name_or_path = "name/or/path/to/hf/llama/13b/model"
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lora_model_name_or_path = "bofenghuang/vigogne-instruct-13b"
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tokenizer = LlamaTokenizer.from_pretrained(base_model_name_or_path, padding_side="right", use_fast=False)
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model = LlamaForCausalLM.from_pretrained(
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base_model_name_or_path,
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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model = PeftModel.from_pretrained(model, lora_model_name_or_path)
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```
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You can infer this model by using the following Google Colab Notebook.
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<a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/notebooks/infer_instruct.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
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## Limitations
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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.
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],
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"model_type": "llama",
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}
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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|
||||
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||||
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||||
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||||
93385
tokenizer.json
Normal file
93385
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
||||
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||||
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33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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|
||||
}
|
||||
Reference in New Issue
Block a user