Instructions to use mpasila/Kunoichi-DPO-v2-Instruct-32k-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mpasila/Kunoichi-DPO-v2-Instruct-32k-7B with Transformers:

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

pipe = pipeline("text-generation", model="mpasila/Kunoichi-DPO-v2-Instruct-32k-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mpasila/Kunoichi-DPO-v2-Instruct-32k-7B")
model = AutoModelForCausalLM.from_pretrained("mpasila/Kunoichi-DPO-v2-Instruct-32k-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mpasila/Kunoichi-DPO-v2-Instruct-32k-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mpasila/Kunoichi-DPO-v2-Instruct-32k-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mpasila/Kunoichi-DPO-v2-Instruct-32k-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mpasila/Kunoichi-DPO-v2-Instruct-32k-7B

SGLang

How to use mpasila/Kunoichi-DPO-v2-Instruct-32k-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 "mpasila/Kunoichi-DPO-v2-Instruct-32k-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": "mpasila/Kunoichi-DPO-v2-Instruct-32k-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 "mpasila/Kunoichi-DPO-v2-Instruct-32k-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": "mpasila/Kunoichi-DPO-v2-Instruct-32k-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mpasila/Kunoichi-DPO-v2-Instruct-32k-7B with Docker Model Runner:
```
docker model run hf.co/mpasila/Kunoichi-DPO-v2-Instruct-32k-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Kunoichi-DPO-v2-Instruct-32k-7B

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

This hopefully gives 32k context for Kunoichi-DPO-v2 model though since it also uses the instruct model it might change its behavior somewhat.

Merge script copied from this ichigoberry/pandafish-2-7b-32k.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using alpindale/Mistral-7B-v0.2-hf 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: alpindale/Mistral-7B-v0.2-hf
    # No parameters necessary for base model
  - model: mistralai/Mistral-7B-Instruct-v0.2
    parameters:
      density: 0.53
      weight: 0.4
  - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
    parameters:
      density: 0.53
      weight: 0.4
merge_method: dare_ties
base_model: alpindale/Mistral-7B-v0.2-hf
parameters:
  int8_mask: true
dtype: bfloat16