Instructions to use hyokwan/familidata_hkcode_gemma2_9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyokwan/familidata_hkcode_gemma2_9b with Transformers:

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

pipe = pipeline("text-generation", model="hyokwan/familidata_hkcode_gemma2_9b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hyokwan/familidata_hkcode_gemma2_9b")
model = AutoModelForCausalLM.from_pretrained("hyokwan/familidata_hkcode_gemma2_9b", 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 hyokwan/familidata_hkcode_gemma2_9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hyokwan/familidata_hkcode_gemma2_9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hyokwan/familidata_hkcode_gemma2_9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hyokwan/familidata_hkcode_gemma2_9b

SGLang

How to use hyokwan/familidata_hkcode_gemma2_9b 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 "hyokwan/familidata_hkcode_gemma2_9b" \
    --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": "hyokwan/familidata_hkcode_gemma2_9b",
		"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 "hyokwan/familidata_hkcode_gemma2_9b" \
        --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": "hyokwan/familidata_hkcode_gemma2_9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hyokwan/familidata_hkcode_gemma2_9b with Docker Model Runner:
```
docker model run hf.co/hyokwan/familidata_hkcode_gemma2_9b
```

Model Details model

model is continued pretrained language model based on google/gemma2-9b-it

This model is trained with specific department of university (Korea Plytechnics Fintech) .

Intended Use TBD

How to use TBD

Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.

Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.

Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.

As part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.

Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.

Misuse

license: apache-2.0

Downloads last month: 7

Safetensors

Model size

9B params

Tensor type

F16

Model tree for hyokwan/familidata_hkcode_gemma2_9b

Base model

google/gemma-2-9b

Finetuned

google/gemma-2-9b-it

Finetuned

(506)

this model

hyokwan
/

familidata_hkcode_gemma2_9b

license: apache-2.0

Model tree for hyokwan/familidata_hkcode_gemma2_9b

Dataset used to train hyokwan/familidata_hkcode_gemma2_9b