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Update v3.0

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README.md CHANGED
@@ -5,48 +5,64 @@ language:
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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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  inference: false
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  ---
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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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-
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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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-
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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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  pipeline_tag: text-generation
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  library_name: transformers
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  tags:
 
8
  - llama
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  - LLM
 
 
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  inference: false
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  ---
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  <p align="center" width="100%">
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+ <img src="https://huggingface.co/bofenghuang/vigogne-13b-instruct/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-13B-Instruct: A French Instruction-following LLaMA Model
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+ Vigogne-13B-Instruct is a LLaMA-13B model fine-tuned to follow the French instructions.
20
 
21
  For more information, please visit the Github repo: https://github.com/bofenghuang/vigogne
22
 
23
  **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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+ ## Changelog
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+
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+ All versions are available in branches.
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+ - **V1.0**: Initial release, trained on the translated Stanford Alpaca dataset.
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+ - **V1.1**: Improved translation quality of the Stanford Alpaca dataset.
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+ - **V2.0**: Expanded training dataset to 224k for better performance.
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+ - **V3.0**: Further expanded training dataset to 262k for improved results.
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+
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+ ## Usage
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  ```python
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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+ from vigogne.preprocess import generate_instruct_prompt
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+
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+ model_name_or_path = "bofenghuang/vigogne-13b-instruct"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side="right", use_fast=False)
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+ model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto")
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+
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+ user_query = "Expliquez la différence entre DoS et phishing."
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+ prompt = generate_instruct_prompt(user_query)
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+ input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device)
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+ input_length = input_ids.shape[1]
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+
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+ generated_outputs = model.generate(
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+ input_ids=input_ids,
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+ generation_config=GenerationConfig(
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+ temperature=0.1,
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+ do_sample=True,
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+ repetition_penalty=1.0,
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+ max_new_tokens=512,
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+ ),
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+ return_dict_in_generate=True,
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  )
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+ generated_tokens = generated_outputs.sequences[0, input_length:]
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+ generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
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+ print(generated_text)
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  ```
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+ You can also 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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adapter_config.json DELETED
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