File size: 2,488 Bytes
9c5e882 b5d9e4d 9c5e882 d20fe65 498e3c6 71566a7 8f9d32f 465bde2 71566a7 d093104 71566a7 d20fe65 8f9d32f e015e6d 8f9d32f d20fe65 9798446 3357384 9798446 2757a1f 9798446 284719e 9798446 284719e 2757a1f 284719e 9798446 284719e 3357384 9798446 48feee2 1a0ed32 48feee2 |
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 |
---
language:
- it
license: apache-2.0
tags:
- text-generation-inference
- text generation
---
# Mistral-7B-v0.1 for Italian Language Text Generation
## Model Architecture
- **Base Model:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Specialization:** Italian Language
## 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 |
|:----------------------------|:----------------------|:----------------|:---------------------|:--------|
| **Accuracy Normalized** | 0.6731 | 0.5502 | 0.5364 | 0.5866 |
---
**Quantized 4-Bit Version Available**
A quantized 4-bit version of the model is available for use. This version offers a more efficient processing capability by reducing the precision of the model's computations to 4 bits, which can lead to faster performance and decreased memory usage. This might be particularly useful for deploying the model on devices with limited computational power or memory resources.
For more details and to access the model, visit the following link: [Mistral-Ita-7b-GGUF 4-bit version](https://huggingface.co/DeepMount00/Mistral-Ita-7b-GGUF).
---
## How to Use
How to utilize my Mistral for Italian text generation
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL_NAME = "DeepMount00/Mistral-Ita-7b"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16).eval()
model.to(device)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
def generate_answer(prompt):
messages = [
{"role": "user", "content": prompt},
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=200, do_sample=True,
temperature=0.001, eos_token_id=tokenizer.eos_token_id)
decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
return decoded[0]
prompt = "Come si apre un file json in python?"
answer = generate_answer(prompt)
print(answer)
```
---
## Developer
[Michele Montebovi] |