File size: 3,164 Bytes
6fbc346 9e0ff22 860a6f7 9e0ff22 6fbc346 dd76069 6fbc346 dd76069 6fbc346 9e0ff22 dd76069 9e0ff22 dd76069 6fbc346 6edba2d 6fbc346 6edba2d 6fbc346 9e0ff22 |
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 |
---
license: apache-2.0
language:
- it
pipeline_tag: text-generation
tags:
- text-generation-inference
- transformers
- mistral
- trl
- sft
base_model: sapienzanlp/Minerva-3B-base-v1.0
datasets:
- mchl-labs/stambecco_data_it
widget:
- text: "Di seguito è riportata un'istruzione che descrive un'attività, abbinata ad un input che fornisce ulteriore informazione. Scrivi una risposta che soddisfi adeguatamente la richiesta. \n### Istruzione:\nSuggerisci un'attività serale romantica\n\n### Input:\n\n### Risposta:"
example_title: Example 1
---
# Model Card for Minerva-3B-Instruct-v1.0
Minerva-3B-Instruct-v1.0 is an instruction-tuned version of the Minerva-3B-base-v1.0 model, specifically fine-tuned for understanding and following instructions in Italian.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Walid Iguider
- **Model type:** Instruction Tuned
- **License:** cc-by-nc-sa-4.0
- **Finetuned from model:**: [Minerva-3B-base-v1.0](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0), developed by [Sapienza NLP](https://nlp.uniroma1.it) in collaboration with [Future Artificial Intelligence Research (FAIR)](https://fondazione-fair.it/) and [CINECA](https://www.cineca.it/)
## Evaluation
For a detailed comparison of model performance, check out the [Leaderboard for Italian Language Models](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard).
Here's a breakdown of the performance metrics:
| Metric | hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average |
|:----------------------------|:----------------------|:----------------|:---------------------|:--------|
| **Minerva-3B-Instruct-v1.0** | 0.5191 | 0.3174 | 0.2629 | 0.3664 |
| Minerva-3B-base-v1.0 | 0.5187 | 0.3045 | 0.2612 | 0,3614 |
### Sample Code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
torch.random.manual_seed(0)
# Run text generation pipeline with our next model
prompt = """Di seguito è riportata un'istruzione che descrive un'attività, abbinata ad un input che fornisce
ulteriore informazione. Scrivi una risposta che soddisfi adeguatamente la richiesta.
### Istruzione:
Suggerisci un'attività serale romantica
### Input:
### Risposta:"""
model_id = "walid-iguider/Minerva-3B-Instruct-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
output = pipe(prompt, **generation_args)
print(output[0]['generated_text'])
```
|