Instructions to use mergekit-community/mergekit-dare_ties-uomphhm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mergekit-community/mergekit-dare_ties-uomphhm with Transformers:

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

pipe = pipeline("text-generation", model="mergekit-community/mergekit-dare_ties-uomphhm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mergekit-community/mergekit-dare_ties-uomphhm")
model = AutoModelForCausalLM.from_pretrained("mergekit-community/mergekit-dare_ties-uomphhm")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mergekit-community/mergekit-dare_ties-uomphhm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mergekit-community/mergekit-dare_ties-uomphhm

SGLang

How to use mergekit-community/mergekit-dare_ties-uomphhm 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 "mergekit-community/mergekit-dare_ties-uomphhm" \
    --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": "mergekit-community/mergekit-dare_ties-uomphhm",
		"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 "mergekit-community/mergekit-dare_ties-uomphhm" \
        --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": "mergekit-community/mergekit-dare_ties-uomphhm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mergekit-community/mergekit-dare_ties-uomphhm with Docker Model Runner:
```
docker model run hf.co/mergekit-community/mergekit-dare_ties-uomphhm
```

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using mistralai/Mistral-Nemo-Base-2407 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: mistralai/Mistral-Nemo-Base-2407
    # No parameters necessary for base model
  - model: mistralai/Mistral-Nemo-Instruct-2407
    parameters:
      density: 0.50  # Mid-level density for general instruction tuning
      weight: 0.25    # Moderate influence for balanced instruction-following
  - model: TheDrummer/Rocinante-12B-v1.1  # Highest influence (strong reasoning/language balance)
    parameters:
      density: 0.60  # Higher density for deeper reasoning and coherence
      weight: 0.35    # Primary influence model
  - model: ReadyArt/Forgotten-Safeword-12B-3.6  # Creativity & conversational nuance
    parameters:
      density: 0.50  # Balanced density for creative and nuanced responses
      weight: 0.20    # Mid-tier influence
  - model: PocketDoc/Dans-SakuraKaze-V1.0.0-12b  # Second highest influence (natural conversation flow)
    parameters:
      density: 0.55  # Slightly high density for fluid conversation
      weight: 0.20    # Substantial influence in dialogue

merge_method: dare_ties
base_model: mistralai/Mistral-Nemo-Base-2407
parameters:
  normalize: true  # Ensures weight distribution remains balanced
  int8_mask: true  # Reduces memory usage while keeping precision
dtype: bfloat16  # Optimal balance between performance and efficiency