.ipynb_checkpoints/README-checkpoint.md

---
language:
- it
pipeline_tag: text-generation
max_length: 100
widget:
- text: Alessandro è un ragazzo che progetta Infissi
- text: Melissa è una ragazza che adora 
tags:
- italian
- italiano
- llama
---
This is a train starting from an empty model based exclusively on Italian language datasets (currently redpajama 2023-14 it)<br/>
<br/>
the train is ongoing and will extend to new datasets.<br/>
<br/>
More precise versions will be published shortly.<br/>
<br/>
Train on my server, i have studied and adapted the model starting from the repository https://github.com/karpathy/llama2.c<br/>
<br/>
- LLama model parameter:
  - max_seq_len: (7b = 2048) The maximum sequence length for input data.
  - dim (7b= 4096) Represents the dimensionalityl
  - n_layers: (7b = 32) The number of layers
  - n_heads: (7b = 32) Determines the number of attention heads
  - n_kv_heads: (7b = 32) The number of key and value heads
  - multiple_of: (7b = 256) A value used to make the SwiGLU hidden layer size a multiple of a large power of 2
<br/>
- Model parameter
  - max_seq_len = 1024
  - dim         = 768
  - n_layers    = 32
  - n_heads     = 32
  - n_kv_heads  = 32
  - multiple_of = 32
<br/>
num decayed parameter tensors: 225, with 251,068,416 parameters<br/>
num non-decayed parameter tensors: 65, with 49,920 parameters<br/>

To just use the model, you can run:

```py
  
  # Load model directly
  from transformers import AutoTokenizer, AutoModelForCausalLM

  # Load the model and tokenizer
  tokenizer_model = AutoTokenizer.from_pretrained("peruginia/Llama-2-Small")
  model = AutoModelForCausalLM.from_pretrained("peruginia/Llama-2-Small")
  model.to('cuda')
  from tokenizer import Tokenizer

  # Define the prompt
  prompt = "Alessandro è un ragazzo che progetta Infissi"

  # Tokenize the prompt
  inputs    = tokenizer_model(prompt, return_tensors="pt").to('cuda')

  # Generate text
  output = model.generate(**inputs, do_sample = True, max_new_tokens=100, top_k = 300, top_p = 0.85, temperature = 1.0, num_return_sequences = 1)

  # Decode and print the generated text
  generated_text = tokenizer_model.decode(output[0], skip_special_tokens=True)

  print(generated_text)
```