Instructions to use mgoin/llama2.c-stories15M-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mgoin/llama2.c-stories15M-ds with Transformers:

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

pipe = pipeline("text-generation", model="mgoin/llama2.c-stories15M-ds")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mgoin/llama2.c-stories15M-ds")
model = AutoModelForCausalLM.from_pretrained("mgoin/llama2.c-stories15M-ds")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mgoin/llama2.c-stories15M-ds with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mgoin/llama2.c-stories15M-ds"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mgoin/llama2.c-stories15M-ds",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mgoin/llama2.c-stories15M-ds

SGLang

How to use mgoin/llama2.c-stories15M-ds 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 "mgoin/llama2.c-stories15M-ds" \
    --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": "mgoin/llama2.c-stories15M-ds",
		"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 "mgoin/llama2.c-stories15M-ds" \
        --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": "mgoin/llama2.c-stories15M-ds",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mgoin/llama2.c-stories15M-ds with Docker Model Runner:
```
docker model run hf.co/mgoin/llama2.c-stories15M-ds
```

https://huggingface.co/Xenova/llama2.c-stories15M exported to be compatible with DeepSparse

from deepsparse import TextGeneration
model = TextGeneration(model="hf:mgoin/llama2.c-stories15M-ds")
out = model("Once upon a time", max_new_tokens=300)
print(out.generations[0].text)

### , there was a little girl named Lily. She loved to play outside in the sunshine. One day, she saw a big, red ball in the sky. It was the sun! She thought it was so pretty.
### Lily wanted to play with the ball, but it was too high up in the sky. She tried to jump and reach it, but she couldn't. Then, she had an idea. She would use a stick to knock the ball down.
### Lily found a stick and tried to hit the ball. But the stick was too short. She tried again and again, but she couldn't reach it. She felt sad.
### Suddenly, a kind man came by and saw Lily. He asked her what was wrong. Lily told him about the ball. The man smiled and said, "I have a useful idea!" He took out a long stick and used it to knock the ball down. Lily was so happy! She thanked the man and they played together in the sunshine. Once upon a time, there was a little girl named Lily. She loved to play outside in the sunshine. One day, she saw a big, red ball in the sky. It was the sun! She thought it was so pretty.
### Lily wanted to play with the ball, but it was too high up in the sky. She tried to jump, but she couldn't reach it

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