Instructions to use shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5 with Transformers:

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

pipe = pipeline("text-generation", model="shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5")
model = AutoModelForCausalLM.from_pretrained("shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5

SGLang

How to use shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5 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 "shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5" \
    --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": "shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5",
		"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 "shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5" \
        --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": "shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5 with Docker Model Runner:
```
docker model run hf.co/shidowake/240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5
```

240402-Swal-MS-7b-CVec-co0.5-mist-inst-v0.1-co0.5-Hermes-2-Pro-co0.5-openchat_3.5 / model-00007-of-00008.safetensors

shidowake

Upload MistralForCausalLM

f4a1c66 verified about 2 years ago

Download

History Blame Contribute Delete

1.98 GB

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:: 24e94d9a9adeff5dd55001fb548f8744ea9377fcbcad8fafe1bccb0d2650944a
Size of remote file:: 1.98 GB
SHA256:: 5aeca90ee07ef592bd3aa9a4a7537cc5dca37e30649bd7b34713179f2e493acb

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