Text Generation
Transformers
Safetensors
English
llama
rp
erp
chat
storywriting
text-generation-inference
Instructions to use MarsupialAI/KitchenSink_103b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarsupialAI/KitchenSink_103b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MarsupialAI/KitchenSink_103b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MarsupialAI/KitchenSink_103b") model = AutoModelForCausalLM.from_pretrained("MarsupialAI/KitchenSink_103b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MarsupialAI/KitchenSink_103b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MarsupialAI/KitchenSink_103b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MarsupialAI/KitchenSink_103b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MarsupialAI/KitchenSink_103b
- SGLang
How to use MarsupialAI/KitchenSink_103b 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 "MarsupialAI/KitchenSink_103b" \ --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": "MarsupialAI/KitchenSink_103b", "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 "MarsupialAI/KitchenSink_103b" \ --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": "MarsupialAI/KitchenSink_103b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MarsupialAI/KitchenSink_103b with Docker Model Runner:
docker model run hf.co/MarsupialAI/KitchenSink_103b
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# Kitchen Sink 103b
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This model is a rotating-stack merge of three 70b models in a 103b (120 layer) configuration inspired by Venus 103b. The result of
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Component models for the rotating stack are
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- royallab/Aetheria-L2-70B
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- lizpreciatior/lzlv_70b_fp16_hf
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- Sao10K/WinterGoddess-1.4x-70B-L2
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Components of those models are purported to include: Nous-Hermes-Llama2-70b, Xwin-LM-7B-V0.1, Mythospice-70b, Euryale-1.3-L2-70B,
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This model is uncensored and perfectly capable of generating objectionable material. However, it is not an explicitely-NSFW model,
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# Prompt format
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Seems to have the strongest affinity for Alpaca prompts, but Vicuna works as well. Considering the variety of components, most
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# WTF is a rotating-stack merge?
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Inspired by Undi's experiments with stacked merges, Jeb Carter found that output quality and model initiative could be significantly
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# Kitchen Sink 103b
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This model is a rotating-stack merge of three 70b models in a 103b (120 layer) configuration inspired by Venus 103b. The result of
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this "frankenmerge" is a large model that contains a little bit of everything - including the kitchen sink. RP, chat, storywriting,
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and instruct are all well supported. It may or may not code well - I lack the expertise to test it in that capacity, but considering
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the source models, it is unlikely.
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Component models for the rotating stack are
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- royallab/Aetheria-L2-70B
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- lizpreciatior/lzlv_70b_fp16_hf
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- Sao10K/WinterGoddess-1.4x-70B-L2
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Components of those models are purported to include: Nous-Hermes-Llama2-70b, Xwin-LM-7B-V0.1, Mythospice-70b, Euryale-1.3-L2-70B,
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tulu-2-dpo-70b, GOAT-70B-Storytelling, Platypus2-70B-instruct, Lila-70B, SunsetBoulevard, and some private LoRAs.
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This model is uncensored and perfectly capable of generating objectionable material. However, it is not an explicitely-NSFW model,
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and it has never "gone rogue" and tried to insert NSFW content into SFW prompts in my experience.
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# Prompt format
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Seems to have the strongest affinity for Alpaca prompts, but Vicuna works as well. Considering the variety of components, most
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formats will probbaly work to some extent.
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# WTF is a rotating-stack merge?
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Inspired by Undi's experiments with stacked merges, Jeb Carter found that output quality and model initiative could be significantly
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improved by reversing the model order in the stack, and then doing a linear merge between the original and reversed stacks. That is
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what I did here. I created three passthrough stacked merges using the three source models (rotating the model order in each stack),
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and then doing a linear merge of all three stacks. The exact merge configs can be found in the recipe.txt file.
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