Instructions to use CoolSpring/gemma-2-9b-it-liaozhai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CoolSpring/gemma-2-9b-it-liaozhai with Transformers:

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

pipe = pipeline("text-generation", model="CoolSpring/gemma-2-9b-it-liaozhai")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CoolSpring/gemma-2-9b-it-liaozhai")
model = AutoModelForCausalLM.from_pretrained("CoolSpring/gemma-2-9b-it-liaozhai")
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

vLLM

How to use CoolSpring/gemma-2-9b-it-liaozhai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CoolSpring/gemma-2-9b-it-liaozhai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CoolSpring/gemma-2-9b-it-liaozhai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CoolSpring/gemma-2-9b-it-liaozhai

SGLang

How to use CoolSpring/gemma-2-9b-it-liaozhai 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 "CoolSpring/gemma-2-9b-it-liaozhai" \
    --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": "CoolSpring/gemma-2-9b-it-liaozhai",
		"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 "CoolSpring/gemma-2-9b-it-liaozhai" \
        --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": "CoolSpring/gemma-2-9b-it-liaozhai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use CoolSpring/gemma-2-9b-it-liaozhai 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 CoolSpring/gemma-2-9b-it-liaozhai 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 CoolSpring/gemma-2-9b-it-liaozhai to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CoolSpring/gemma-2-9b-it-liaozhai to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="CoolSpring/gemma-2-9b-it-liaozhai",
    max_seq_length=2048,
)

Docker Model Runner
How to use CoolSpring/gemma-2-9b-it-liaozhai with Docker Model Runner:
```
docker model run hf.co/CoolSpring/gemma-2-9b-it-liaozhai
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is a fine-tune of gemma-2-9b-it, trained for 3 epochs on a synthetic dataset created from the book Liaozhai Zhiyi. The stories in the book were translated by an LLM to modern Simplified Chinese and paired with generated writing prompts. The untranslated version of the stories can be found in this dataset: CoolSpring/liaozhai-zhiyi.

Liaozhai Zhiyi, also known as Strange Tales from a Chinese Studio, is a collection of approximately 500 stories written in the traditional Chinese Zhiguai and Chuanqi styles by Pu Songling. The aim of this fine-tuning attempt is to explore incorporating these characteristics into the storytelling capabilities of a specific model.

Disclaimer: Users should be aware that due to the historical nature of the training materials, it may generate biased content that reflects the cultural norms and perspectives of the author's era (late 17th to early 18th century China). These outputs may not align with contemporary values and should be interpreted with appropriate historical context.

Q4_K_M GGUF: CoolSpring/gemma-2-9b-it-liaozhai-Q4_K_M-GGUF

Prompt Template - gemma

<bos><start_of_turn>user
{input}<end_of_turn>
<start_of_turn>model
{output}<end_of_turn>

Users must adhere to Gemma Terms of Use when using this model.

Unsloth Metadata

Developed by: CoolSpring
License: gemma
Finetuned from model : unsloth/gemma-2-9b-it-bnb-4bit

This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 7

Safetensors

Model size

9B params

Tensor type

F16

Model tree for CoolSpring/gemma-2-9b-it-liaozhai

Base model

google/gemma-2-9b

Finetuned

google/gemma-2-9b-it

Quantized

unsloth/gemma-2-9b-it-bnb-4bit

Finetuned

(255)

this model

Quantizations

1 model