Create README.md

README.md

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+license: apache-2.0
+datasets:
+- cosimoiaia/Loquace-102k
+language:
+- it
+tags:
+- Italian
+- Qlora
+- finetuning
+- Text Generation
+pipeline_tag: text-generation
+---
+Model Card for Loquace-Wizard-13B [(Versione in Italiano tradotta da Loquace)](https://huggingface.co/cosimoiaia/Loquace-7B-Mistral/blob/main/Readme-ITA.md)
+
+# 🇮🇹 Loquace-7B-Mistral v0.1 🇮🇹 
+
+Loquace is an Italian speaking, instruction finetuned, Large Language model. 🇮🇹
+
+Loquace-Wizard-14B's peculiar features:
+
+- The First 13B Specifically finetuned in Italian.
+- Is pretty good a following istructions in Italian.
+- Responds well to prompt-engineering.
+- Works well in a RAG (Retrival Augmented Generation) setup.
+- It has been trained on a relatively raw dataset [Loquace-102K](https://huggingface.co/datasets/cosimoiaia/Loquace-102k) using QLoRa and Mistral-7B-Instruct as base.
+- Training took only 8 hours on a 3090, costing a little more than <b>2 euro</b>! On [Genesis Cloud](https://gnsiscld.co/26qhlf) GPU.
+- It is <b><i>Truly Open Source</i></b>: Model, Dataset and Code to replicate the results are completely released.
+- Created in a garage in the south of Italy.
+
+The Loquace Italian LLM models are created with the goal of democratizing AI and LLM in the Italian Landscape. 
+
+<b>No more need for expensive GPU, large funding, Big Corporation or Ivory Tower Institution, just download the code and train on your dataset on your own PC (or a cheap and reliable cloud provider like [Genesis Cloud](https://gnsiscld.co/26qhlf) )</b>
+
+### Fine-tuning Instructions:
+The related code can be found at:
+https://github.com/cosimoiaia/Loquace
+
+## Inference:
+
+```python
+from transformers import LlamaForCausalLM, AutoTokenizer
+
+
+def generate_prompt(instruction):    
+   prompt = f"""### Instruction: {instruction}
+   
+### Response:
+"""
+   return prompt
+
+model_name = "."
+
+model = LlamaForCausalLM.from_pretrained(
+   model_name,
+   device_map="auto",
+   torch_dtype=torch.bfloat16                
+)
+
+model.config.use_cache = True
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, add_eos_token=False)
+
+prompt = generate_prompt("Chi era Dante Alighieri?")
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, do_sample = True, num_beams = 2, top_k=50, top_p= 0.95, max_new_tokens=2046, early_stopping = True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("Response:")[1].strip())
+```
+## Model Author:
+Cosimo Iaia <cosimo.iaia@gmail.com>