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Model: raicrits/Hermes7b_ITA
Source: Original Platform

.gitattributes

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README.md

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+---
+license: other
+datasets:
+- raicrits/Orca_ITA_200k
+language:
+- it
+pipeline_tag: text-generation
+tags:
+- LLM
+- Italian
+- Orca
+- Hermes
+- LLama2
+library_name: transformers
+---
+
+
+# Model Card for Model raicrits/Hermes7b_ITA
+
+<!-- Provide a quick summary of what the model is/does. -->
+An open-source LLaMa2 language model of 7b parameters fine-tuned (using as base model [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b)) to follow instructions in italian.
+
+### Model Description
+
+This model is a LLM of 7b parameters based on [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b), a version of [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b) fine-tuned to follow instructions.
+The model was further fine-tuned in order to follow instructions in italian, using [LoRA](https://arxiv.org/abs/2106.09685) approach and a dataset of 120k random pairs of instruction/answer from [raicrits/Orca_ITA_200k](https://huggingface.co/datasets/raicrits/Orca_ITA_200k).
+
+This repository contains the model weights merged with the LoRA adapters obtained in the fine-tuning procedure.
+
+
+- **Developed by:** Stefano Scotta (stefano.scotta@rai.it)
+- **Model type:** LLM fine-tuned to follow instructions
+- **Language(s) (NLP):** Italian
+- **License:** Other
+- **Finetuned from model:** [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b)
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The model can be used as is to respond to simple instructions in Italian or can be further fine-tuned to perform specific tasks.
+
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+As any other LLM it is possible that the model generates content which does not correspond to the reality as well as wrong, biased, offensive and inappropriate answers.
+
+
+## How to Get Started with the Model
+ **Prompt template:**
+
+ ``` python
+"""### Instruction: {instruction}
+
+### Response:
+"""
+```
+
+ **Usage:**
+Use the code below to get started with the model.
+ ``` python
+import os
+import torch
+import sys
+from transformers import LlamaForCausalLM, AutoTokenizer
+
+
+def generate_prompt_test(instruction):    
+    prompt = f"""### Instruction: {instruction}
+    
+### Response:
+"""
+    return prompt
+
+model_name = "raicrits/Hermes7b_ITA"
+
+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_test("Cosa puoi dirmi sul dio Hermes?")
+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=256, early_stopping = True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("Response:")[1].strip())
+```
+``` python
+"""Hermes è un dio dell'antica Grecia. Era il dio del commercio, della comunicazione e del trasporto. Era anche il dio della mente e della intelligenza. Era noto per il suo eloquente linguaggio e la sua capacità di spostarsi velocemente. Era considerato il messaggero degli dèi e spesso veniva raffigurato con un cappello di pelle di capra e sandali."""
+```
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+The model was fine-tuned on 120k random records of  [raicrits/Orca_ITA_200k](https://huggingface.co/datasets/raicrits/Orca_ITA_200k).
+
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The fine-tuning procedure was done using [LoRA](https://arxiv.org/abs/2106.09685) approach. 
+
+
+#### Training Hyperparameters
+
+ **Training setting:**
+- train epochs=3,
+
+- learning_rate=2e-4,
+
+- mixed precision training: float16
+
+
+ **LoRA configuration:**
+-  r= 8
+  
+- lora_alpha=16
+  
+- target_modules=["q_proj","v_proj"]
+
+- lora_dropout=0.05
+  
+- bias="none"
+  
+- task_type=TaskType.CAUSAL_LM
+
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** 1 NVIDIA A100/40Gb
+- **Hours used:** 78
+- **Cloud Provider:** Private Infrastructure
+- **Carbon Emitted:** 8.42 kg eq. CO2
+
+
+
+## Model Card Authors
+
+Stefano Scotta (stefano.scotta@rai.it)
+
+## Model Card Contact
+
+stefano.scotta@rai.it

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