Instructions to use jsfs11/RandomMergeNoNorm-7B-DARETIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsfs11/RandomMergeNoNorm-7B-DARETIES with Transformers:

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

pipe = pipeline("text-generation", model="jsfs11/RandomMergeNoNorm-7B-DARETIES")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jsfs11/RandomMergeNoNorm-7B-DARETIES")
model = AutoModelForMultimodalLM.from_pretrained("jsfs11/RandomMergeNoNorm-7B-DARETIES")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jsfs11/RandomMergeNoNorm-7B-DARETIES with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsfs11/RandomMergeNoNorm-7B-DARETIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsfs11/RandomMergeNoNorm-7B-DARETIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jsfs11/RandomMergeNoNorm-7B-DARETIES

SGLang

How to use jsfs11/RandomMergeNoNorm-7B-DARETIES 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 "jsfs11/RandomMergeNoNorm-7B-DARETIES" \
    --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": "jsfs11/RandomMergeNoNorm-7B-DARETIES",
		"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 "jsfs11/RandomMergeNoNorm-7B-DARETIES" \
        --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": "jsfs11/RandomMergeNoNorm-7B-DARETIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jsfs11/RandomMergeNoNorm-7B-DARETIES with Docker Model Runner:
```
docker model run hf.co/jsfs11/RandomMergeNoNorm-7B-DARETIES
```

RandomMergeNoNorm-7B-DARETIES / model-00005-of-00008.safetensors

jsfs11

Upload folder using huggingface_hub

528c859 verified over 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:: 776a3b23851097ec0044548c80e2405c5b2ecaac72e8d5c1e6e20b6a134e3e92
Size of remote file:: 1.98 GB
SHA256:: 96ae78def3e5f8ef70c7a72e062f74c5db1aeff7920298618a2149dae44e6310

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