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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES")
model = AutoModelForCausalLM.from_pretrained("jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsfs11/RandomMergeNoNormWEIGHTED-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/RandomMergeNoNormWEIGHTED-7B-DARETIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

RandomMergeNoNormWEIGHTED-7B-DARETIES / mergekit_config.yml

jsfs11

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	models:
	- model: FelixChao/WestSeverus-7B-DPO-v2
	# No parameters necessary for base model
	- model: FelixChao/WestSeverus-7B-DPO-v2
	parameters:
	density: [1, 0.7, 0.1]
	weight: [0, 0.3, 0.7, 1]
	- model: CultriX/Wernicke-7B-v9
	parameters:
	density: [1, 0.7, 0.3]
	weight: [0, 0.25, 0.5, 1]
	- model: mlabonne/NeuralBeagle14-7B
	parameters:
	density: 0.25
	weight:
	- filter: mlp
	value: 0.5
	- value: 0
	merge_method: ties
	base_model: FelixChao/WestSeverus-7B-DPO-v2
	parameters:
	int8_mask: true
	normalize: true
	sparsify:
	- filter: mlp
	value: 0.5
	- filter: self_attn
	value: 0.5
	dtype: float16