Instructions to use 922CA/Silicon-Natsuki-7b-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 922CA/Silicon-Natsuki-7b-v0.5 with Transformers:

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

pipe = pipeline("text-generation", model="922CA/Silicon-Natsuki-7b-v0.5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("922CA/Silicon-Natsuki-7b-v0.5")
model = AutoModelForCausalLM.from_pretrained("922CA/Silicon-Natsuki-7b-v0.5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 922CA/Silicon-Natsuki-7b-v0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "922CA/Silicon-Natsuki-7b-v0.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "922CA/Silicon-Natsuki-7b-v0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/922CA/Silicon-Natsuki-7b-v0.5

SGLang

How to use 922CA/Silicon-Natsuki-7b-v0.5 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 "922CA/Silicon-Natsuki-7b-v0.5" \
    --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": "922CA/Silicon-Natsuki-7b-v0.5",
		"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 "922CA/Silicon-Natsuki-7b-v0.5" \
        --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": "922CA/Silicon-Natsuki-7b-v0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use 922CA/Silicon-Natsuki-7b-v0.5 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 922CA/Silicon-Natsuki-7b-v0.5 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 922CA/Silicon-Natsuki-7b-v0.5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 922CA/Silicon-Natsuki-7b-v0.5 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="922CA/Silicon-Natsuki-7b-v0.5",
    max_seq_length=2048,
)

Docker Model Runner
How to use 922CA/Silicon-Natsuki-7b-v0.5 with Docker Model Runner:
```
docker model run hf.co/922CA/Silicon-Natsuki-7b-v0.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Silicon-Natsuki-7b-v0.5

Yet another test fine-tune, this time for Natsuki character from DDLC per a request
Fine-tuned on a WIP dataset of ~800+ items (dialogue scraped from game augmented by Mistral to turn each into snippets of multi-turn chat dialogue between Player and Natsuki + manually edited items feeding info about character such as height, hair color, etc.)
Base: SanjiWatsuki/Silicon-Maid-7B (Mistral)
GGUF
Lora here

USAGE

For best results: replace "Human" and "Assistant" with "Player" and "Natsuki" like so:

\nPlayer: (prompt)\nNatsuki:

HYPERPARAMS

Trained for 1 epoch
rank: 32
lora alpha: 32
lora dropout: 0
lr: 2e-4
batch size: 2
grad steps: 4

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

WARNINGS AND DISCLAIMERS

This model is meant to closely reflect the characteristics of Natsuki. Despite this, there is always the chance that "Natsuki" will hallucinate and get information about herself wrong or act out of character (for example, in testing she knows her own club and its members, and even her height and favorite ice cream flavor, but may still get her info wrong like thinking she's club president).

Finally, this model is not guaranteed to output aligned or safe outputs, use at your own risk.

Downloads last month: 5

Model tree for 922CA/Silicon-Natsuki-7b-v0.5

Base model

SanjiWatsuki/Silicon-Maid-7B

Finetuned

(3)

this model

Quantizations

2 models

922CA
/

Silicon-Natsuki-7b-v0.5

Silicon-Natsuki-7b-v0.5

USAGE

HYPERPARAMS

WARNINGS AND DISCLAIMERS

Model tree for 922CA/Silicon-Natsuki-7b-v0.5

Dataset used to train 922CA/Silicon-Natsuki-7b-v0.5