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
```

nielsr HF Staff commited on Apr 29, 2025

Commit

2cfe84d

verified ·

1 Parent(s): cda188c

Add pipeline tag and library name

Browse files

This PR adds the `pipeline_tag` and `library_name` to the model card metadata. This will improve discoverability of the model on the Hugging Face Hub. Specifically, it will allow users to find this model via the `text-generation` pipeline tag.

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -1,8 +1,10 @@
 ---
-license: apache-2.0
 language:
 - it
 - en
 ---
 # Mistral-7B-v0.1-Italian-RANDOM

 ---
 language:
 - it
 - en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
 # Mistral-7B-v0.1-Italian-RANDOM