Upload README.md

README.md

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+---
+license: other
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+tags:
+- transformers
+- gguf
+- imatrix
+- Azzurro
+---
+Quantizations of https://huggingface.co/MoxoffSpA/Azzurro
+
+
+# From original readme
+
+## Usage
+
+Be sure to install these dependencies before running the program
+
+```python
+!pip install transformers torch sentencepiece
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cpu" # if you want to use the gpu make sure to have cuda toolkit installed and change this to "cuda"
+
+model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Azzurro")
+tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Azzurro")
+
+question = """Quanto è alta la torre di Pisa?"""
+context = """
+La Torre di Pisa è un campanile del XII secolo, famoso per la sua inclinazione. Alta circa 56 metri.
+"""
+
+prompt = f"Domanda: {question}, contesto: {context}"
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
+
+model_inputs = encodeds.to(device)
+model.to(device)
+
+generated_ids = model.generate(
+    model_inputs, # The input to the model
+    max_new_tokens=128, # Limiting the maximum number of new tokens generated
+    do_sample=True, # Enabling sampling to introduce randomness in the generation
+    temperature=0.1, # Setting temperature to control the randomness, lower values make it more deterministic
+    top_p=0.95, # Using nucleus sampling with top-p filtering for more coherent generation       
+    eos_token_id=tokenizer.eos_token_id # Specifying the token that indicates the end of a sequence
+)
+
+decoded_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+trimmed_output = decoded_output.strip()
+print(trimmed_output)
+```