Instructions to use LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2

SGLang

How to use LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2 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 "LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2" \
    --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": "LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2",
		"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 "LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2" \
        --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": "LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2
```

SlimOrca Mixtral 8x7B

Official release of the SlimOrca Mixtral finetune. More details to come.

Model Details

Model Description

Developed by: OpenAccess AI Collective and OpenOrca
Finetuned from model [optional]: mistralai/Mixtral-8x7B-v0.1

Downloads last month: 12

Model tree for LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2

Base model

mistralai/Mixtral-8x7B-v0.1

Finetuned

(80)

this model

LoneStriker
/

Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2

SlimOrca Mixtral 8x7B

Model Details

Model Description

Model tree for LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2

Dataset used to train LoneStriker/Mixtral-SlimOrca-8x7B-2.4bpw-h6-exl2-2