Instructions to use MergekitCloud/mergekit-165 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MergekitCloud/mergekit-165 with Transformers:

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

pipe = pipeline("image-text-to-text", model="MergekitCloud/mergekit-165")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MergekitCloud/mergekit-165")
model = AutoModelForMultimodalLM.from_pretrained("MergekitCloud/mergekit-165", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MergekitCloud/mergekit-165 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MergekitCloud/mergekit-165"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MergekitCloud/mergekit-165",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MergekitCloud/mergekit-165

SGLang

How to use MergekitCloud/mergekit-165 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 "MergekitCloud/mergekit-165" \
    --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": "MergekitCloud/mergekit-165",
		"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 "MergekitCloud/mergekit-165" \
        --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": "MergekitCloud/mergekit-165",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MergekitCloud/mergekit-165 with Docker Model Runner:
```
docker model run hf.co/MergekitCloud/mergekit-165
```

merged-model

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 Qwen/Qwen3.6-35B-A3B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


merge_method: dare_ties
base_model: Qwen/Qwen3.6-35B-A3B
models:
  - model: InternScience/Agents-A1
    parameters:
      density: 0.6
      weight: 0.5
  - model: deepreinforce-ai/Ornith-1.0-35B
    parameters:
      density: 0.6
      weight: 0.5
  - model: Qwen/Qwen3.6-35B-A3B
    parameters:
      density: 0.25
      weight: 0.25
dtype: bfloat16

Downloads last month: 138

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for MergekitCloud/mergekit-165

InternScience/Agents-A1

Qwen/Qwen3.6-35B-A3B

ornith-ai/Ornith-1.0-35B

Merge model

this model

Paper for MergekitCloud/mergekit-165

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 38