Instructions to use ivangrapher/merged_champion_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ivangrapher/merged_champion_v2 with Transformers:

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

pipe = pipeline("text-generation", model="ivangrapher/merged_champion_v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ivangrapher/merged_champion_v2")
model = AutoModelForCausalLM.from_pretrained("ivangrapher/merged_champion_v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ivangrapher/merged_champion_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ivangrapher/merged_champion_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ivangrapher/merged_champion_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ivangrapher/merged_champion_v2

SGLang

How to use ivangrapher/merged_champion_v2 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 "ivangrapher/merged_champion_v2" \
    --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": "ivangrapher/merged_champion_v2",
		"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 "ivangrapher/merged_champion_v2" \
        --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": "ivangrapher/merged_champion_v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ivangrapher/merged_champion_v2 with Docker Model Runner:
```
docker model run hf.co/ivangrapher/merged_champion_v2
```

merged_champion_v2 / mergekit_config.yml

ivangrapher

Upload folder using huggingface_hub

03051d8 verified 5 months ago

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	base_model: dura-lori/affine-5ED5dwT4fztHjgjyR6vXpbGfnooeuWfr3VueaZrrfWJSou7y
	dtype: bfloat16
	merge_method: dare_ties
	modules:
	default:
	slices:
	- sources:
	- layer_range: [0, 64]
	model: dura-lori/affine-5ED5dwT4fztHjgjyR6vXpbGfnooeuWfr3VueaZrrfWJSou7y
	parameters:
	weight: 0.45
	- layer_range: [0, 64]
	model: catKnowCoffiee/Affine2-5EPhxsSDWnNzYjZdupuC5WLi2a5M8FYfnkvo5ukWM8Yge9zi
	parameters:
	weight: 0.3
	- layer_range: [0, 64]
	model: dura-lori/affine-5FcYc4MZ2z9yfFp6qPBQQjtS3cXkDV7x46ZUcoUP3pFRGoj4
	parameters:
	weight: 0.15
	- layer_range: [0, 64]
	model: leary-comos/affine-5CSqun1nmHbJQuvxyvJ534ZBpbFUUT1hoWXAuj18k7Qs7g2R
	parameters:
	weight: 0.1
	parameters:
	density: 0.3
	normalize: 1.0