Instructions to use cgus/Mistral-7B-Holodeck-1-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cgus/Mistral-7B-Holodeck-1-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="cgus/Mistral-7B-Holodeck-1-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cgus/Mistral-7B-Holodeck-1-exl2")
model = AutoModelForCausalLM.from_pretrained("cgus/Mistral-7B-Holodeck-1-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cgus/Mistral-7B-Holodeck-1-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cgus/Mistral-7B-Holodeck-1-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cgus/Mistral-7B-Holodeck-1-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cgus/Mistral-7B-Holodeck-1-exl2

SGLang

How to use cgus/Mistral-7B-Holodeck-1-exl2 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 "cgus/Mistral-7B-Holodeck-1-exl2" \
    --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": "cgus/Mistral-7B-Holodeck-1-exl2",
		"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 "cgus/Mistral-7B-Holodeck-1-exl2" \
        --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": "cgus/Mistral-7B-Holodeck-1-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cgus/Mistral-7B-Holodeck-1-exl2 with Docker Model Runner:
```
docker model run hf.co/cgus/Mistral-7B-Holodeck-1-exl2
```

Mistral 7B - Holodeck exl2

Original model: Mistral-7B-Holodeck-1
Model creator: KoboldAI

Quants

4bpw-h6 (main)
4.25bpw-h6
4.65bpw-h6
5bpw-h6
6bpw-h6
8bpw-h8

Quantization notes

Made with exllamav2 0.0.15 with the default dataset.

How to run

This quantization method uses GPU and requires Exllamav2 loader which can be found in following applications:

Text Generation Webui

KoboldAI

ExUI

Original card

Mistral 7B - Holodeck

Model Description

Mistral 7B-Holodeck is a finetune created using Mistral's 7B model.

Training data

The training data contains around 3000 ebooks in various genres. Most parts of the dataset have been prepended using the following text: [Genre: <genre1>, <genre2>]

### Limitations and Biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).

Downloads last month: 4