File size: 4,505 Bytes
e0332b4 506dae4 e0332b4 506dae4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
---
license: llama2
language:
- it
tags:
- text-generation-inference
---
# Model Card for LLaMAntino-2-chat-7b-UltraChat-ITA
## Model description
<!-- Provide a quick summary of what the model is/does. -->
**LLaMAntino-2-chat-7b-UltraChat** is a *Large Language Model (LLM)* that is an instruction-tuned version of **LLaMAntino-2-chat-7b** (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-chat
- **Language(s) (NLP):** Italian
- **License:** Llama 2 Community License
- **Finetuned from model:** [swap-uniba/LLaMAntino-2-chat-7b-hf-ITA](https://huggingface.co/swap-uniba/LLaMAntino-2-chat-7b-hf-ITA)
## 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
"<s>[INST] <<SYS>>\n" \
"Sei un assistente disponibile, rispettoso e onesto. " \
"Rispondi sempre nel modo piu' 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 e' 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 AutoModelForCausalLM, AutoTokenizer
model_id = "swap-uniba/LLaMAntino-2-chat-7b-hf-UltraChat-ITA"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
user_msg = "Ciao! Come stai?"
prompt = "<s>[INST] <<SYS>>\n" \
"Sei un assistente disponibile, rispettoso e onesto. " \
"Rispondi sempre nel modo piu' 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 e' 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} [/INST] "
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids, max_length=1024)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy()[:, input_ids.shape[1]:], skip_special_tokens=True)[0])
```
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*: The model loading strategy above requires the [*bitsandbytes*](https://pypi.org/project/bitsandbytes/) and [*accelerate*](https://pypi.org/project/accelerate/) libraries
## 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:
*Coming soon*! |