Instructions to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with Transformers:

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

pipe = pipeline("text-generation", model="xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k")
model = AutoModelForCausalLM.from_pretrained("xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k

SGLang

How to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k 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 "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k with Docker Model Runner:
```
docker model run hf.co/xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k
```

NeuralKunoichi-EroSumika-4x7B-128k

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---
license: apache-2.0
language:
- en
tags:
- merge
---

![image/png](https://i.ibb.co/MRXkh6p/icon2.png)

Test merge. Attempt to get good at RP, ERP, general tasks model with 128k context. Every model here has [Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context](https://huggingface.co/Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context) in merge instead of regular MistralYarn 128k. The reason is because i belive Epiculous merged it with Mistral Instruct v0.2 to make first 32k context experience as perfect as possible until we reach YaRN from 32 to 128k, if not - it's sad D:, or, i get something wrong.

[Exl2, 4.0 bpw](https://huggingface.co/xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k-exl2-bpw-4.0)

[GGUF](https://huggingface.co/xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k-GGUF)

Here is the "family tree" of this model, im not writing full model names cause they long af
### NeuralKunoichi-EroSumika 4x7B 128k
```
* NeuralKunoichi-EroSumika 4x7B
	*(1) Kunocchini-7b-128k
	|
	*(2) Mistral-Instruct-v0.2-128k
		* Mistral-7B-Instruct-v0.2
		|
		* Fett-128k
	|
	*(3) Erosumika-128k
		* Erosumika 7B
		|
		* FFett-128k
	|
	*(4) Mistral-NeuralHuman-128k
		* Fett-128k
		|
		* Mistral-NeuralHuman
			* Mistral_MoreHuman
			|
			* Mistral-Neural-Story
```


## Models used

- [localfultonextractor/Erosumika-7B](https://huggingface.co/localfultonextractor/Erosumika-7B)
- [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- [Test157t/Kunocchini-7b-128k-test](https://huggingface.co/Test157t/Kunocchini-7b-128k-test)
- [NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story](https://huggingface.co/NeuralNovel/Mistral-7B-Instruct-v0.2-Neural-Story)
- [valine/MoreHuman](https://huggingface.co/valine/MoreHuman)
- [Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context](https://huggingface.co/Epiculous/Fett-uccine-Long-Noodle-7B-120k-Context)