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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Download README.md from HardbanRecordsLab/Mistral-7B-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 446 Bytes
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https://huggingface.co/HardbanRecordsLab/Mistral-7B-Instruct/resolve/main/README.md
- Command line
-
hf download hf://HardbanRecordsLab/Mistral-7B-Instruct/README.md
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curl -L -o README.md https://huggingface.co/HardbanRecordsLab/Mistral-7B-Instruct/resolve/main/README.md
446 Bytes
| 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 | |