Instructions to use serpdotai/sparsetral-16x7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use serpdotai/sparsetral-16x7B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="serpdotai/sparsetral-16x7B-v1", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("serpdotai/sparsetral-16x7B-v1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use serpdotai/sparsetral-16x7B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "serpdotai/sparsetral-16x7B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "serpdotai/sparsetral-16x7B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/serpdotai/sparsetral-16x7B-v1

SGLang

How to use serpdotai/sparsetral-16x7B-v1 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 "serpdotai/sparsetral-16x7B-v1" \
    --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": "serpdotai/sparsetral-16x7B-v1",
		"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 "serpdotai/sparsetral-16x7B-v1" \
        --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": "serpdotai/sparsetral-16x7B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use serpdotai/sparsetral-16x7B-v1 with Docker Model Runner:
```
docker model run hf.co/serpdotai/sparsetral-16x7B-v1
```

prompt format

### System:\n{system}\n### Human:\n{user}\n### Assistant:\n"

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("serpdotai/sparsetral-16x7B-v1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("serpdotai/sparsetral-16x7B-v1", device_map="auto", trust_remote_code=True).eval()

inputs = tokenizer('### System:\n\n### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
# I am doing well, thank you.

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Safetensors

Model size

8B params

Tensor type

BF16

serpdotai
/

sparsetral-16x7B-v1

Usage

Dataset used to train serpdotai/sparsetral-16x7B-v1