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peruginia/
Llama-2-Small

Name: Llama-2-Small
Availability: InStock

text-generation-inference

endpoints_compatible

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This is a train starting from an empty model based exclusively on Italian language datasets (currently redpajama 2023-14 it)

the train is ongoing and will extend to new datasets.

More precise versions will be published shortly.

Train on my server, i have studied and adapted the model starting from the repository https://github.com/karpathy/llama2.c

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
Model parameter
- max_seq_len = 1024
- dim = 768
- n_layers = 32
- n_heads = 32
- n_kv_heads = 32
- multiple_of = 32

num decayed parameter tensors: 225, with 251,068,416 parameters
num non-decayed parameter tensors: 65, with 49,920 parameters

To just use the model, you can run:

  
  # 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)