Instructions to use Locutusque/Rhino-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/Rhino-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/Rhino-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/Rhino-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("Locutusque/Rhino-Mistral-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Locutusque/Rhino-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/Rhino-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/Rhino-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/Rhino-Mistral-7B

SGLang

How to use Locutusque/Rhino-Mistral-7B 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 "Locutusque/Rhino-Mistral-7B" \
    --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": "Locutusque/Rhino-Mistral-7B",
		"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 "Locutusque/Rhino-Mistral-7B" \
        --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": "Locutusque/Rhino-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/Rhino-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/Locutusque/Rhino-Mistral-7B
```

Convert CO2 emissions from kg to g

by selenecodes - opened Jun 4, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-1

selenecodes

Jun 4, 2024

Good afternoon, I've noticed your CO2 emissions might've been off. If you're using CodeCarbon which reports the CO2 emissions in KG then this commit should be correct since it converts them from kg to grams as per the HuggingFace docs https://huggingface.co/docs/hub/en/model-cards-co2. If the training of your model indeed only costed 8.88grams to train then disregard this commit

Convert CO2 emissions from kg to g71250fd2

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