Instructions to use grimjim/franken-kunoichi-IDUS-11B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grimjim/franken-kunoichi-IDUS-11B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="grimjim/franken-kunoichi-IDUS-11B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("grimjim/franken-kunoichi-IDUS-11B") model = AutoModelForCausalLM.from_pretrained("grimjim/franken-kunoichi-IDUS-11B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use grimjim/franken-kunoichi-IDUS-11B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "grimjim/franken-kunoichi-IDUS-11B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "grimjim/franken-kunoichi-IDUS-11B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/grimjim/franken-kunoichi-IDUS-11B
- SGLang
How to use grimjim/franken-kunoichi-IDUS-11B 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 "grimjim/franken-kunoichi-IDUS-11B" \ --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": "grimjim/franken-kunoichi-IDUS-11B", "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 "grimjim/franken-kunoichi-IDUS-11B" \ --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": "grimjim/franken-kunoichi-IDUS-11B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use grimjim/franken-kunoichi-IDUS-11B with Docker Model Runner:
docker model run hf.co/grimjim/franken-kunoichi-IDUS-11B
franken-kunoichi-IDUS-11B
This is a merge of pre-trained language models created using mergekit.
The Interwoven Depth Up-Scaling merge formula was adapted from Sanji Watsuki's longcat-10.7B.
I consider this to be a negative result, but perhaps an interesting one. I've tested casually with temperature 0.7-1.2 and minP 0.01-0.03, with both Alpaca and ChatML prompts. The resulting generated text is interesting for RP due to chaotic variation, mostly sticking to grammatically correct output, but easily veers too far into chaos (e.g., abruptly switching language) while having difficulty tracking things. Given that, the inherited 8K context length is of dubious benefit.
Maybe additional training might smooth the transition between models, but that hypothesis is untested.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0, 8]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [8, 9]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [8, 9]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [9, 10]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [9, 10]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [10, 11]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [10, 11]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [11, 12]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [11, 12]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [12, 13]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [12, 13]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [13, 14]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [13, 14]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [14, 15]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [14, 15]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [15, 16]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [15, 16]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [16, 17]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [16, 17]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [17, 18]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [17, 18]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [18, 19]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [18, 19]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [19, 20]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [19, 20]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [20, 21]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [20, 21]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [21, 22]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [21, 22]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [22, 23]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [22, 23]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [23, 24]
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [23, 24]
- sources:
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [24, 32]
merge_method: passthrough
dtype: float16
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