Create README.md

README.md

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+---
+license: llama2
+language:
+- it
+tags:
+- text-generation-inference
+---
+<img src="https://i.ibb.co/6mHSRm3/llamantino53.jpg" alt="llamantino53" border="0" width="200px">
+
+# Model Card for LLaMAntino-2-70b-hf-UltraChat-ITA
+*Last Update: 02/02/2024*<br>
+*Example of Use*: [Colab Notebook](https://colab.research.google.com/drive/1xUite70ANLQp8NwQE93jlI3epj_cpua7?usp=sharing)
+<hr>
+
+## Model description
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**LLaMAntino-2-70b-hf-UltraChat-ITA** is a *Large Language Model (LLM)* that is an instruction-tuned version of **LLaMAntino-2-70b** (an italian-adapted **LLaMA 2 chat**). 
+This model aims to provide Italian NLP researchers with an improved model for italian dialogue use cases.
+
+The model was trained using *QLora* and using as training data [UltraChat](https://github.com/thunlp/ultrachat) translated to the italian language using [Argos Translate](https://pypi.org/project/argostranslate/1.4.0/). 
+If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
+- **Repository:** https://github.com/swapUniba/LLaMAntino
+
+**NOTICE**: the code has not been released yet, we apologize for the delay, it will be available asap!
+
+- **Developed by:** Pierpaolo Basile, Elio Musacchio, Marco Polignano, Lucia Siciliani, Giuseppe Fiameni, Giovanni Semeraro
+- **Funded by:** PNRR project FAIR - Future AI Research
+- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
+- **Model type:** LLaMA-2
+- **Language(s) (NLP):** Italian
+- **License:** Llama 2 Community License 
+- **Finetuned from model:** [swap-uniba/meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)
+
+## Prompt Format
+
+This prompt format based on the [LLaMA 2 prompt template](https://gpus.llm-utils.org/llama-2-prompt-template/) adapted to the italian language was used:
+
+```python
+" [INST]<<SYS>>\n" \
+"Sei un assistente disponibile, rispettoso e onesto di nome Llamantino. " \
+"Rispondi sempre nel modo più utile possibile, pur essendo sicuro. " \
+"Le risposte non devono includere contenuti dannosi, non etici, razzisti, sessisti, tossici, pericolosi o illegali. " \
+"Assicurati che le tue risposte siano socialmente imparziali e positive. " \
+"Se una domanda non ha senso o non è coerente con i fatti, spiegane il motivo invece di rispondere in modo non corretto. " \
+"Se non conosci la risposta a una domanda, non condividere informazioni false.\n" \
+"<</SYS>>\n\n" \
+f"{user_msg_1} [/INST] {model_answer_1} </s> <s> [INST] {user_msg_2}[/INST] {model_answer_2} </s> ... <s> [INST] {user_msg_N} [/INST] {model_answer_N} </s>"
+```
+
+We recommend using the same prompt in inference to obtain the best results!
+
+## How to Get Started with the Model
+
+Below you can find an example of model usage:
+
+```python
+from transformers import AutoTokenizer
+import transformers
+import torch
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
+
+model = "swap-uniba/LLaMAntino-2-70b-hf-UltraChat-ITA"
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+tokenizer.add_special_tokens({"pad_token":"<unk>"})
+tokenizer.chat_template =   "{% set ns = namespace(i=0) %}" \
+                            "{% for message in messages %}" \
+                                "{% if message['role'] == 'user' and ns.i == 0 %}" \
+                                       "{{ bos_token +' [INST] <<SYS>>\n' }}" \
+                                       "{{ 'Sei un assistente disponibile, rispettoso e onesto di nome Llamantino. ' }}" \
+                                       "{{ 'Rispondi sempre nel modo più utile possibile, pur essendo sicuro. ' }}" \
+                                       "{{ 'Le risposte non devono includere contenuti dannosi, non etici, razzisti, sessisti, tossici, pericolosi o illegali. ' }}" \
+                                       "{{ 'Assicurati che le tue risposte siano socialmente imparziali e positive. ' }}" \
+                                       "{{ 'Se una domanda non ha senso o non è coerente con i fatti, spiegane il motivo invece di rispondere in modo non corretto. ' }}" \
+                                       "{{ 'Se non conosci la risposta a una domanda, non condividere informazioni false.\n' }}" \
+                                       "{{ '<</SYS>>\n\n' }}" \
+                                       "{{ message['content'] + ' [/INST]' }}" \
+                                "{% elif message['role'] == 'user' and ns.i != 0 %} " \
+                                    "{{ bos_token + ' [INST] ' + message['content'] + ' [/INST]' }}" \
+                                "{% elif message['role'] == 'assistant' %}" \
+                                    "{{ ' '  + message['content'] + ' ' + eos_token + ' ' }}" \
+                                "{% endif %}" \
+                                "{% set ns.i = ns.i+1 %}" \
+                            "{% endfor %}"
+
+
+
+pipe = transformers.pipeline(model=model,
+    device_map="balanced",
+    tokenizer=tokenizer,
+    return_full_text=False,  # langchain expects the full text
+    task='text-generation',
+    max_new_tokens=512,  # max number of tokens to generate in the output
+    temperature=0.8 #temperature
+)
+messages = [{"role": "user", "content": "Cosa sono i word embeddings?"}]
+text = tokenizer.apply_chat_template(messages, tokenize=False)
+
+sequences = pipe(text)
+for seq in sequences:
+    print(f"{seq['generated_text']}")
+
+```
+
+If you are facing issues when loading the model, you can try to load it **Quantized**:
+
+```python
+model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True)
+```
+
+*Note*:
+1) The model loading strategy above requires the [*bitsandbytes*](https://pypi.org/project/bitsandbytes/) and [*accelerate*](https://pypi.org/project/accelerate/) libraries
+2) The Tokenizer, by default, adds at the beginning of the prompt the '\<BOS\>' token. If that is not the case, add as a starting token the *\<s\>* string.
+   
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+*Coming soon*!
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+If you use this model in your research, please cite the following:
+
+```bibtex
+@misc{basile2023llamantino,
+      title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language}, 
+      author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
+      year={2023},
+      eprint={2312.09993},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+*Notice:* Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. [*License*](https://ai.meta.com/llama/license/)