Instructions to use SemanticAlignment/Mistral-v0.1-Italian-LAPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SemanticAlignment/Mistral-v0.1-Italian-LAPT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SemanticAlignment/Mistral-v0.1-Italian-LAPT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SemanticAlignment/Mistral-v0.1-Italian-LAPT")
model = AutoModelForCausalLM.from_pretrained("SemanticAlignment/Mistral-v0.1-Italian-LAPT", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SemanticAlignment/Mistral-v0.1-Italian-LAPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SemanticAlignment/Mistral-v0.1-Italian-LAPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SemanticAlignment/Mistral-v0.1-Italian-LAPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SemanticAlignment/Mistral-v0.1-Italian-LAPT

SGLang

How to use SemanticAlignment/Mistral-v0.1-Italian-LAPT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SemanticAlignment/Mistral-v0.1-Italian-LAPT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SemanticAlignment/Mistral-v0.1-Italian-LAPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SemanticAlignment/Mistral-v0.1-Italian-LAPT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SemanticAlignment/Mistral-v0.1-Italian-LAPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SemanticAlignment/Mistral-v0.1-Italian-LAPT with Docker Model Runner:
```
docker model run hf.co/SemanticAlignment/Mistral-v0.1-Italian-LAPT
```

iperbole commited on Apr 30, 2025

Commit

891190d

verified ·

1 Parent(s): f2d2273

Update README.md

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@@ -16,7 +16,7 @@ library_name: transformers
 The **Mistral-7B-v0.1-Adapted** collection of large language models (LLMs), is a collection of adapted generative models in 7B (text in/text out), adapted models from **Mistral-7B-Base-v0.1**.
-*Mistral-v0.1-Italian-LAPT* is a continual trained mistral model.
 **Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
@@ -24,7 +24,7 @@ The **Mistral-7B-v0.1-Adapted** collection of large language models (LLMs), is a
 ## Data used for the adaptation
-The **Mistral-7B-v0.1-Adapted** model are trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
 The data are extracted to be skewed toward Italian language with a ration of one over four. Extracting the first 9B tokens from Italian part of CulturaX and the first 3B tokens from English part of CulturaX.
@@ -32,7 +32,7 @@ The data are extracted to be skewed toward Italian language with a ration of one
 You can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
-Make sure to update your transformers installation via pip install --upgrade transformers.
 ```python
 import transformers
@@ -47,6 +47,8 @@ pipeline = transformers.pipeline(
 pipeline("Cosa si può fare in una bella giornata di sole?")
 ```
 ## Citation
 If you use any part of this work, please consider citing the paper as follows:

 The **Mistral-7B-v0.1-Adapted** collection of large language models (LLMs), is a collection of adapted generative models in 7B (text in/text out), adapted models from **Mistral-7B-Base-v0.1**.
+Mistral-v0.1-Italian-FVT is a continually trained Mistral model.
 **Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
 ## Data used for the adaptation
+The **Mistral-7B-v0.1-Adapted** models are trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
 The data are extracted to be skewed toward Italian language with a ration of one over four. Extracting the first 9B tokens from Italian part of CulturaX and the first 3B tokens from English part of CulturaX.
 You can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
 ```python
 import transformers
 pipeline("Cosa si può fare in una bella giornata di sole?")
 ```
+Code: https://github.com/SapienzaNLP/sava
 ## Citation
 If you use any part of this work, please consider citing the paper as follows: