Instructions to use estrogen/ms24b-exprmerge-v0a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use estrogen/ms24b-exprmerge-v0a with Transformers:

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

pipe = pipeline("text-generation", model="estrogen/ms24b-exprmerge-v0a")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("estrogen/ms24b-exprmerge-v0a")
model = AutoModelForCausalLM.from_pretrained("estrogen/ms24b-exprmerge-v0a", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use estrogen/ms24b-exprmerge-v0a with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "estrogen/ms24b-exprmerge-v0a"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "estrogen/ms24b-exprmerge-v0a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/estrogen/ms24b-exprmerge-v0a

SGLang

How to use estrogen/ms24b-exprmerge-v0a 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 "estrogen/ms24b-exprmerge-v0a" \
    --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": "estrogen/ms24b-exprmerge-v0a",
		"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 "estrogen/ms24b-exprmerge-v0a" \
        --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": "estrogen/ms24b-exprmerge-v0a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use estrogen/ms24b-exprmerge-v0a with Docker Model Runner:
```
docker model run hf.co/estrogen/ms24b-exprmerge-v0a
```

ms24b-exprmerge-v0a / model-00010-of-00010.safetensors

Fizzarolli

Upload folder using huggingface_hub

7364f16 verified over 1 year ago

Download

History Blame Contribute Delete

3.77 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:: e8eb648dcb3c605cc5eac5fd2b72e9ec4176f3a96400817bb4568198fd5d9a28
Size of remote file:: 3.77 GB
SHA256:: 09d02e55564d274f6365bf3b6065e8c6c1b714f8feec6c7db32986052d882f46

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