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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

iperbole commited on May 2, 2025

Commit

c3871b7

verified ·

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Update README.md

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@@ -5,6 +5,8 @@ language:
 license: apache-2.0
 pipeline_tag: text-generation
 library_name: transformers
 ---
 # Mistral-7B-v0.1-Italian-RANDOM
@@ -16,13 +18,13 @@ 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-RANDOM* 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
-**Model Architecture:** Mistral-7B-v0.1-Adapted is an auto-regressive language model that uses an optimized transformer architecture.
 ## Data used for the adaptation
@@ -34,7 +36,7 @@ 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
@@ -49,6 +51,8 @@ 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:

 license: apache-2.0
 pipeline_tag: text-generation
 library_name: transformers
+base_model:
+- mistralai/Mistral-7B-v0.1
 ---
 # Mistral-7B-v0.1-Italian-RANDOM
 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-RANDOM* is a continually 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
+**Model Architecture:** Mistral-7B-v0.1-Adapted are auto-regressive language models that uses an optimized transformer architecture.
 ## Data used for the adaptation
 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: