Instructions to use Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2")
model = AutoModelForMultimodalLM.from_pretrained("Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2

SGLang

How to use Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2 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 "Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2" \
    --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": "Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2",
		"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 "Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2" \
        --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": "Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/Natkituwu/Erosumika-7B-v3-0.2-5.0bpw-exl2
```

Erosumika-7B-v3-0.2

~Mistral 0.2 Edition~

5.0bpw quant of Erosumika 7b 0.2 v3. Original Link : (https://huggingface.co/localfultonextractor/Erosumika-7B-v3-0.2)

Model Details

The Mistral 0.2 version of Erosumika-7B-v3, a DARE TIES merge between Nitral's Kunocchini-7b, Endevor's InfinityRP-v1-7B and my FlatErosAlpha, a flattened(in order to keep the vocab size 32000) version of tavtav's eros-7B-ALPHA. Alpaca and ChatML work best. Slightly smarter and better prompt comprehension than Mistral 0.1 Erosumika-7B-v3. 32k context should work.

GGUF quants

Limitations and biases

The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.

merge_method: task_arithmetic
base_model: alpindale/Mistral-7B-v0.2-hf
models:
  - model: localfultonextractor/Erosumika-7B-v3
    parameters:
      weight: 1.0
dtype: float16

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Erosumika 0.2 v3 7b

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exl2 quants of Erosumika 0.2 v3 7b • 6 items • Updated Mar 27, 2024