Instructions to use Xenova/tiny-random-MistralForCausalLM_external-data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xenova/tiny-random-MistralForCausalLM_external-data with Transformers:

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

pipe = pipeline("text-generation", model="Xenova/tiny-random-MistralForCausalLM_external-data")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xenova/tiny-random-MistralForCausalLM_external-data")
model = AutoModelForCausalLM.from_pretrained("Xenova/tiny-random-MistralForCausalLM_external-data", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Xenova/tiny-random-MistralForCausalLM_external-data with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Xenova/tiny-random-MistralForCausalLM_external-data

SGLang

How to use Xenova/tiny-random-MistralForCausalLM_external-data 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 "Xenova/tiny-random-MistralForCausalLM_external-data" \
    --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": "Xenova/tiny-random-MistralForCausalLM_external-data",
		"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 "Xenova/tiny-random-MistralForCausalLM_external-data" \
        --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": "Xenova/tiny-random-MistralForCausalLM_external-data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Xenova/tiny-random-MistralForCausalLM_external-data with Docker Model Runner:
```
docker model run hf.co/Xenova/tiny-random-MistralForCausalLM_external-data
```

tiny-random-MistralForCausalLM_external-data / onnx /model.onnx

Xenova HF Staff

Upload 8 files

692e713 verified over 2 years ago

History Blame Contribute Delete

104 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: b289391d5c621d526858d0814cb3f2d8e6e94a022de434f9b873bf3ba4cba249
Size of remote file:: 104 kB
SHA256:: 88c5d1384f3e678492fa927409743bb76fe7a1d13798c443aef44fcdb1ef0b6d

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.