Initial commit

.gitignore

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+checkpoint-*/
+
+tmp*

README.md

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+---
+license: openrail
+language:
+- fr
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- alpaca
+- llama
+- LLM
+datasets:
+- tatsu-lab/alpaca
+inference: false
+---
+
+<p align="center" width="100%">
+<img src="https://huggingface.co/bofenghuang/vigogne-lora-30b/resolve/main/vigogne_logo.png" alt="Vigogne" style="width: 40%; min-width: 300px; display: block; margin: auto;">
+</p>
+
+# Vigogne-LoRA-30b: A French Instruct LLaMA Model
+
+Vigogne-LoRA-30b is a [LLaMA-30B](https://huggingface.co/decapoda-research/llama-30b-hf) model fine-tuned on the translated [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset to follow the 🇫🇷 French instructions.
+
+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
+
+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.
+
+```python
+from peft import PeftModel
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-30b-hf")
+model = LlamaForCausalLM.from_pretrained(
+    "decapoda-research/llama-30b-hf",
+    load_in_8bit=True,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(model, "bofenghuang/vigogne-lora-30b")
+```
+
+You can infer this model by using the following Google Colab Notebook.
+
+<a href="https://colab.research.google.com/github/bofenghuang/vigogne/blob/main/infer.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.
+
+## Next Steps
+
+- Add output examples

adapter_config.json

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+{
+  "base_model_name_or_path": "decapoda-research/llama-30b-hf",
+  "bias": "none",
+  "enable_lora": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "lora_alpha": 16,
+  "lora_dropout": 0.05,
+  "merge_weights": false,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

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