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