Update readme

README.md

@@ -22,15 +22,48 @@ For more information, please visit the Github repo: https://github.com/bofenghua
 
 **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
+## Changelog
+
+All versions are available in branches.
+
+- v1.0: Initial release, trained on the translated Stanford Alpaca dataset.
+- v1.1: Improved translation quality of the Stanford Alpaca dataset.
+- v2.0: Expanded training dataset to 224k for better performance.
+- v3.0: Further expanded training dataset to 262k for improved results.
 
-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.
+## Usage
 
 ```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from vigogne.preprocess import generate_instruct_prompt
+
+model_name_or_path = "bofenghuang/vigogne-7b-instruct"
+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_instruct_prompt(user_query)
+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,
+        #no_repeat_ngram_size=no_repeat_ngram_size,
+        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.
+You can also infer this model by using the following Google Colab Notebook.
 
 <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>