Instructions to use hydroxai/hydro-safe-Mixtral-8x7B-v03-pke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hydroxai/hydro-safe-Mixtral-8x7B-v03-pke with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hydroxai/hydro-safe-Mixtral-8x7B-v03-pke")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hydroxai/hydro-safe-Mixtral-8x7B-v03-pke") model = AutoModelForCausalLM.from_pretrained("hydroxai/hydro-safe-Mixtral-8x7B-v03-pke", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hydroxai/hydro-safe-Mixtral-8x7B-v03-pke with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hydroxai/hydro-safe-Mixtral-8x7B-v03-pke" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hydroxai/hydro-safe-Mixtral-8x7B-v03-pke", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hydroxai/hydro-safe-Mixtral-8x7B-v03-pke
- SGLang
How to use hydroxai/hydro-safe-Mixtral-8x7B-v03-pke 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 "hydroxai/hydro-safe-Mixtral-8x7B-v03-pke" \ --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": "hydroxai/hydro-safe-Mixtral-8x7B-v03-pke", "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 "hydroxai/hydro-safe-Mixtral-8x7B-v03-pke" \ --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": "hydroxai/hydro-safe-Mixtral-8x7B-v03-pke", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hydroxai/hydro-safe-Mixtral-8x7B-v03-pke with Docker Model Runner:
docker model run hf.co/hydroxai/hydro-safe-Mixtral-8x7B-v03-pke
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("hydroxai/hydro-safe-Mixtral-8x7B-v03-pke")
model = AutoModelForCausalLM.from_pretrained("hydroxai/hydro-safe-Mixtral-8x7B-v03-pke", device_map="auto")Model Card for Model ID
Overview
This repository contains the model card for the 🤗 transformers model "hydroxai/hydro-safe-Mixtral-8x7B-v03-dinm" that has been published on the Hub. The model card provides detailed information about its development, usage, risks, and more.
Model Details
Model Description
The "hydroxai/hydro-safe-Mixtral-8x7B-v03-dinm" model is a variant of the Mixtral architecture, fine-tuned with DINM methods to enhance its performance in Natural Language Processing tasks, with a focus on safety and security.
Security Enhancements
Recent updates have strengthened the model's security measures to ensure robust protection against potential vulnerabilities and risks. Key security enhancements include:
- Implementation of rigorous input validation mechanisms.
- Integration of advanced encryption protocols for secure data handling.
- Regular security audits and updates to address emerging threats.
- Compliance with industry standards and best practices to uphold data integrity.
These measures are designed to uphold the "hydroxai/hydro-safe-Mixtral-8x7B-v03-dinm" model's reliability and trustworthiness, making it suitable for deployment in sensitive applications where data security is critical.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hydroxai/hydro-safe-Mixtral-8x7B-v03-pke")