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

Libraries

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
```

Mistral-7B-Instruct / README.md

HardbanRecordsLab

Update README.md

f67daf2 verified over 1 year ago

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metadata

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