Instructions to use grimjim/kunoichi-squared-model_stock-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/kunoichi-squared-model_stock-7B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/kunoichi-squared-model_stock-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/kunoichi-squared-model_stock-7B")
model = AutoModelForCausalLM.from_pretrained("grimjim/kunoichi-squared-model_stock-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use grimjim/kunoichi-squared-model_stock-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/kunoichi-squared-model_stock-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/kunoichi-squared-model_stock-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/kunoichi-squared-model_stock-7B

SGLang

How to use grimjim/kunoichi-squared-model_stock-7B 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/kunoichi-squared-model_stock-7B" \
    --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/kunoichi-squared-model_stock-7B",
		"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/kunoichi-squared-model_stock-7B" \
        --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/kunoichi-squared-model_stock-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use grimjim/kunoichi-squared-model_stock-7B with Docker Model Runner:
```
docker model run hf.co/grimjim/kunoichi-squared-model_stock-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

kunoichi-squared-model_stock-7B

This is a merge of pre-trained language models created using mergekit. The base model used is literally the base model. While the result is syntactically stable, there was something about the resulting narrative generation that seemed off. Perhaps more than 2 models are required for successful model stock merges. Tested primarily with temperature 1-1.1 and minP 1.01-1.03 using ChatML prompts.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using mistralai/Mistral-7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: mistralai/Mistral-7B-v0.1
dtype: float16
merge_method: model_stock
slices:
- sources:
  - layer_range: [0, 32]
    model: mistralai/Mistral-7B-v0.1
  - layer_range: [0, 32]
    model: SanjiWatsuki/Kunoichi-7B
  - layer_range: [0, 32]
    model: SanjiWatsuki/Kunoichi-DPO-v2-7B