Update README.md

README.md

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-license: unknown
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+---
+license: unknown
+---
+
+# Model Card raicrits/Llama3_ChangeOfTopic
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+LoRa adapters for [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) obtained through a finetuning process (using LoRA technique) aimed at making the model capable of detecting
+a change of topic in a given text. 
+
+
+### Model Description
+
+
+The model resulting from the application of the adapters in this repository to the base model [meta-llama/MMeta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) is optimized to perform the 
+specific task of detecting a change of topic in a given text. Given a text the model answers with "1" in the case that it detects a change of topic and "0" otherwise.
+The training has been done using the chapters in the Youtube videos contained in the train split of the dataset [raicrits/YouTube_RAI_dataset](https://huggingface.co/meta-llama/raicrits/YouTube_RAI_dataset).
+Because of the finetuning process it is important to respect the prompt template in order to get good results.
+
+
+- **Developed by:** Stefano Scotta ([email protected])
+- **Model type:** LLM finetuned on the specific task of assign tags to news articles
+- **Language(s) (NLP):** Italian
+- **License:** unknown
+- **Finetuned from model [optional]:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+
+
+## Uses
+
+The model can be used to check if in a given text occurs a chagne of topic or not.
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+
+## Bias, Risks, and 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
+
+Use the code below to get started with the model.
+
+ **Usage:**
+Use the code below to get started with the model.
+ ``` python
+import os
+import torch
+import sys
+from transformers import LlamaForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+model_id = "meta-llama/Meta-Llama-3-8B"
+lora_id = "raicrits/Llama3_ChangeOfTopic"
+
+quantization_config = BitsAndBytesConfig(
+            load_in_8bit=True)
+
+base_model = AutoModelForCausalLM.from_pretrained(model_id, 
+                                             quantization_config=quantization_config, 
+                                             device_map=device)
+model = PeftModel.from_pretrained(base_model, lora_id)
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+messages = [
+    {"role": "system", "content": "You are an AI assistant able to detect change of topics in given texts."},
+    {"role": "user", "content": f"""Analyze the following text written in italian and in case you detect a change of topic answer just with "1", otherwise, if the topic remains the same within all the given text answer just "0". do not add further text.
+    
+Text: {'<text>'}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt").to(model.device)
+
+with torch.no_grad():        
+    outputs = model.generate(
+        input_ids,
+        max_new_tokens=1,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=0.2
+        )
+    response = outputs[0][input_ids.shape[-1]:]
+print(tokenizer.decode(response, skip_special_tokens=False))
+```
+
+## Training Details
+
+### Training Data
+
+Chapters in the Youtube videos contained in the train split of the dataset [raicrits/YouTube_RAI_dataset](https://huggingface.co/meta-llama/raicrits/YouTube_RAI_dataset)
+
+### Training Procedure
+
+The fine-tuning procedure was done using [LoRA](https://arxiv.org/abs/2106.09685) approach.
+
+ **Training setting:**
+- train epochs=1,
+
+- learning_rate=2e-05
+
+- mixed precision training: int8
+
+
+ **LoRA configuration:**
+-  r= 8
+  
+- lora_alpha=16
+  
+- target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]
+
+- lora_dropout=0.1
+  
+- bias="none"
+  
+- task_type=CAUSAL_LM
+
+
+
+[More Information Needed]
+
+## 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:** 45
+- **Cloud Provider:** Private Infrastructure
+- **Carbon Emitted:** 4.86kg eq. CO2
+
+## Model Card Authors
+
+Stefano Scotta ([email protected])
+
+## Model Card Contact
+
+[email protected]