Instructions to use DedeProGames/Wisp-15M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/Wisp-15M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/Wisp-15M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DedeProGames/Wisp-15M-Instruct")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/Wisp-15M-Instruct", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DedeProGames/Wisp-15M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/Wisp-15M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/Wisp-15M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DedeProGames/Wisp-15M-Instruct

SGLang

How to use DedeProGames/Wisp-15M-Instruct 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 "DedeProGames/Wisp-15M-Instruct" \
    --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": "DedeProGames/Wisp-15M-Instruct",
		"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 "DedeProGames/Wisp-15M-Instruct" \
        --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": "DedeProGames/Wisp-15M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DedeProGames/Wisp-15M-Instruct with Docker Model Runner:
```
docker model run hf.co/DedeProGames/Wisp-15M-Instruct
```

Wisp-15M-Instruct / generation_config.json

DedeProGames

Add Wisp-15M-Instruct: 2-epoch SFT on smol-smoltalk with ChatML, EOS-weighted loss

9f22815 verified 12 days ago

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207 Bytes

{
  "bos_token_id": 0,
  "pad_token_id": 1,
  "eos_token_id": [
    3,
    0
  ],
  "do_sample": true,
  "temperature": 0.7,
  "top_p": 0.9,
  "repetition_penalty": 1.1,
  "max_new_tokens": 512
}

	{
	"bos_token_id": 0,
	"pad_token_id": 1,
	"eos_token_id": [
	3,
	0
	],
	"do_sample": true,
	"temperature": 0.7,
	"top_p": 0.9,
	"repetition_penalty": 1.1,
	"max_new_tokens": 512
	}