Instructions to use openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext with Transformers:

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

pipe = pipeline("text-generation", model="openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext")
model = AutoModelForCausalLM.from_pretrained("openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext

SGLang

How to use openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext 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 "openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext" \
    --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": "openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext",
		"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 "openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext" \
        --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": "openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext with Docker Model Runner:
```
docker model run hf.co/openerotica/Mistral-7B-Instruct-v0.1-GPTQ-32g-wikitext
```

This is the instruct model quantized on the wikitext2 dataset at 8192 sequence length. Act order true and 32 groupsize.

Model Card for Mistral-7B-v0.1

The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.

For full details of this model please read our Release blog post

Model Architecture

Mistral-7B-v0.1 is a transformer model, with the following architecture choices:

Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

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