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

Libraries

How to use KimJY/wwppbase with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KimJY/wwppbase")
model = AutoModelForCausalLM.from_pretrained("KimJY/wwppbase")
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 KimJY/wwppbase with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KimJY/wwppbase

SGLang

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

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

wwppbase / README.md

KimJY

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	---
	license: cc-by-sa-4.0
	---

	# Synatra-10.7B-v0.4🐧
	![Synatra-10.7B-v0.4](./Synatra.png)

	# License

	The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-sa-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences.
	# Model Details

	Base Model
	[upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)

	Trained On
	A100 80GB * 1

	Instruction format

	It follows Alpaca format.

	# Model Benchmark


	## Ko-LLM-Leaderboard

	On Benchmarking...

	# Implementation Code

	Since, chat_template already contains insturction format above.
	You can use the code below.

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	device = "cuda" # the device to load the model onto

	model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-10.7B-v0.4")
	tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-10.7B-v0.4")

	messages = [
	{"role": "user", "content": "바나나는 원래 하얀색이야?"},
	]

	encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

	model_inputs = encodeds.to(device)
	model.to(device)

	generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
	decoded = tokenizer.batch_decode(generated_ids)
	print(decoded[0])
	```