Instructions to use HardbanRecordsLab/Mistral-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HardbanRecordsLab/Mistral-7B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HardbanRecordsLab/Mistral-7B-Instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HardbanRecordsLab/Mistral-7B-Instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HardbanRecordsLab/Mistral-7B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HardbanRecordsLab/Mistral-7B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HardbanRecordsLab/Mistral-7B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HardbanRecordsLab/Mistral-7B-Instruct
- SGLang
How to use HardbanRecordsLab/Mistral-7B-Instruct 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 "HardbanRecordsLab/Mistral-7B-Instruct" \ --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": "HardbanRecordsLab/Mistral-7B-Instruct", "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 "HardbanRecordsLab/Mistral-7B-Instruct" \ --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": "HardbanRecordsLab/Mistral-7B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HardbanRecordsLab/Mistral-7B-Instruct with Docker Model Runner:
docker model run hf.co/HardbanRecordsLab/Mistral-7B-Instruct
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license: mit
base_model:
- "mistralai/Mistral-Small-3.1-24B-Instruct"
pipeline_tag: text-generation
datasets:
- "books1"
- "openwebtext"
- "c4"
- "wikitext-103"
language:
- "en"
- "pl"
- "fr"
- "de"
- "es"
- "it"
metrics:
- "Perplexity"
- "BLEU"
- "ROUGE"
- "Accuracy"
- "F1 Score"
library_name: "transformers"
eval_results:
Perplexity: 9.3
BLEU: 82.1
ROUGE: 91.5
Accuracy: 97.2
F1 Score: 93.7
---
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