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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SemanticAlignment/Mistral-v0.1-Italian-FVT")
model = AutoModelForCausalLM.from_pretrained("SemanticAlignment/Mistral-v0.1-Italian-FVT")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SemanticAlignment/Mistral-v0.1-Italian-FVT 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-FVT"
# 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-FVT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use SemanticAlignment/Mistral-v0.1-Italian-FVT 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-FVT" \
    --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-FVT",
		"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-FVT" \
        --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-FVT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

nielsr HF Staff commited on Apr 29, 2025

Commit

30e8a45

verified ·

1 Parent(s): 82d8aae

Add pipeline tag, library name and Github link

Browse files

This PR adds the `pipeline_tag` and `library_name` to the model card metadata, and includes a link to the GitHub repository for easier access to the code. The `pipeline_tag` is set to `text-generation` as this is a language model. The `library_name` is set to `transformers` because the model is used with the `transformers` library.

Files changed (1) hide show

README.md +9 -5

README.md CHANGED Viewed

@@ -1,8 +1,10 @@
 ---
-license: apache-2.0
 language:
 - it
 - en
 ---
 # Mistral-7B-v0.1-Italian-FVT
@@ -14,9 +16,9 @@ language:
 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 continual trained mistral model, after tokenizer substitution.
-The tokenizer of this models after adaptation is the same of [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
 **Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
@@ -24,7 +26,7 @@ The tokenizer of this models after adaptation is the same of [Minverva-3B](https
 ## 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 +34,7 @@ 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 +49,8 @@ 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:

 ---
 language:
 - it
 - en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 # Mistral-7B-v0.1-Italian-FVT
 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, after tokenizer substitution.
+The tokenizer of this model after adaptation is the same as [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
 **Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
 ## Data used for the adaptation
+The **Mistral-7B-v0.1-Adapted** model is 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/Andrew-Wyn/Italian-LLM-Adaptation
 ## Citation
 If you use any part of this work, please consider citing the paper as follows: