wizard-vicuna-13B-SuperHOT-8K-fp16

Instructions to use gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16

SGLang

How to use gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16 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 "gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16" \
    --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": "gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16",
		"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 "gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16" \
        --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": "gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16 with Docker Model Runner:
```
docker model run hf.co/gsaivinay/wizard-vicuna-13B-SuperHOT-8K-fp16
```

wizard-vicuna-13B-SuperHOT-8K-fp16 / huggingface-metadata.txt

gsaivinay

Duplicate from TheBloke/wizard-vicuna-13B-SuperHOT-8K-fp16

feb06c1 about 3 years ago

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522 Bytes

	url: https://huggingface.co/TheBloke/wizard-vicuna-13B-SuperHOT-8K-fp16
	branch: main
	download date: 2023-06-30 04:50:50
	sha256sum:
	44fb43b331a35a7b6cdebcf4b3952ac435742cd6fe04c71155912aa36db9186c pytorch_model-00001-of-00003.bin
	6494699287f79959d68995c0ece4638371ea6e7d28d3cc0d86bf1f01e9ea4169 pytorch_model-00002-of-00003.bin
	db94f4bee1864d8bc1e38ca01f7c1ccd91ba93616025ddc027e84c4fdc341343 pytorch_model-00003-of-00003.bin
	9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 tokenizer.model