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 / README.md

Fizzarolli

Upload folder using huggingface_hub

7364f16 verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

2.61 kB

	---
	base_model:
	- PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
	- lars1234/Mistral-Small-24B-Instruct-2501-writer
	- mistralai/Mistral-Small-24B-Instruct-2501
	- trashpanda-org/Llama3-24B-Mullein-v1
	- unsloth/Mistral-Small-24B-Base-2501
	- arcee-ai/Arcee-Blitz
	- allura-org/Mistral-Small-24b-Sertraline-0304
	library_name: transformers
	tags:
	- mergekit
	- mergekitty
	- merge

	---
	# v0a

	This is a merge of pre-trained language models created using [mergekitty](https://github.com/allura-org/mergekitty).

	## Merge Details
	### Merge Method

	This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [unsloth/Mistral-Small-24B-Base-2501](https://huggingface.co/unsloth/Mistral-Small-24B-Base-2501) as a base.

	### Models Merged

	The following models were included in the merge:
	* [PocketDoc/Dans-PersonalityEngine-V1.2.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.2.0-24b)
	* [lars1234/Mistral-Small-24B-Instruct-2501-writer](https://huggingface.co/lars1234/Mistral-Small-24B-Instruct-2501-writer)
	* [mistralai/Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501)
	* [trashpanda-org/Llama3-24B-Mullein-v1](https://huggingface.co/trashpanda-org/Llama3-24B-Mullein-v1)
	* [arcee-ai/Arcee-Blitz](https://huggingface.co/arcee-ai/Arcee-Blitz)
	* [allura-org/Mistral-Small-24b-Sertraline-0304](https://huggingface.co/allura-org/Mistral-Small-24b-Sertraline-0304)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	base_model: unsloth/Mistral-Small-24B-Base-2501
	merge_method: sce
	dtype: float32
	out_dtype: bfloat16
	models:
	- model: allura-org/Mistral-Small-24b-Sertraline-0304
	parameters:
	select_topk: 0.50
	- model: lars1234/Mistral-Small-24B-Instruct-2501-writer
	parameters:
	select_topk: 0.20
	- model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
	parameters:
	select_topk: 0.20
	- model: trashpanda-org/Llama3-24B-Mullein-v1
	parameters:
	select_topk: 0.175
	- model: arcee-ai/Arcee-Blitz
	parameters:
	select_topk: 0.15
	- model: mistralai/Mistral-Small-24B-Instruct-2501
	parameters:
	select_topk: 0.15

	# apt install git nano -y
	# uv tool install mergekitty --with hf_transfer
	# uv tool install https://github.com/aphrodite-engine/aphrodite-engine/releases/download/v0.6.7/aphrodite_engine-0.6.7-cp38-abi3-manylinux1_x86_64.whl --with aphrodite-engine --with setuptools --with hf_transfer
	# uv tool install huggingface_hub
	# huggingface-cli login
	# nano merge.yml
	# mergekitty-yaml --cuda --lazy-unpickle merge.yml v0a

	```