Instructions to use lillycham/tiny-lm-stories-12m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lillycham/tiny-lm-stories-12m with Transformers:

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

pipe = pipeline("text-generation", model="lillycham/tiny-lm-stories-12m")

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

tokenizer = AutoTokenizer.from_pretrained("lillycham/tiny-lm-stories-12m")
model = AutoModelForCausalLM.from_pretrained("lillycham/tiny-lm-stories-12m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lillycham/tiny-lm-stories-12m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lillycham/tiny-lm-stories-12m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lillycham/tiny-lm-stories-12m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lillycham/tiny-lm-stories-12m

SGLang

How to use lillycham/tiny-lm-stories-12m 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 "lillycham/tiny-lm-stories-12m" \
    --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": "lillycham/tiny-lm-stories-12m",
		"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 "lillycham/tiny-lm-stories-12m" \
        --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": "lillycham/tiny-lm-stories-12m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lillycham/tiny-lm-stories-12m with Docker Model Runner:
```
docker model run hf.co/lillycham/tiny-lm-stories-12m
```

tiny-lm-stories-12m / instruct /config.json

lillycham

Upload folder using huggingface_hub

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{
  "architectures": [
    "GPT2LMHeadModel"
  ],
  "model_type": "gpt2",
  "vocab_size": 4096,
  "n_positions": 256,
  "n_ctx": 256,
  "n_embd": 384,
  "n_layer": 6,
  "n_head": 6,
  "n_inner": 1536,
  "activation_function": "gelu_new",
  "layer_norm_epsilon": 1e-05,
  "resid_pdrop": 0.0,
  "embd_pdrop": 0.0,
  "attn_pdrop": 0.0,
  "initializer_range": 0.02,
  "tie_word_embeddings": true,
  "bos_token_id": 4095,
  "eos_token_id": 4095
}

	{
	"architectures": [
	"GPT2LMHeadModel"
	],
	"model_type": "gpt2",
	"vocab_size": 4096,
	"n_positions": 256,
	"n_ctx": 256,
	"n_embd": 384,
	"n_layer": 6,
	"n_head": 6,
	"n_inner": 1536,
	"activation_function": "gelu_new",
	"layer_norm_epsilon": 1e-05,
	"resid_pdrop": 0.0,
	"embd_pdrop": 0.0,
	"attn_pdrop": 0.0,
	"initializer_range": 0.02,
	"tie_word_embeddings": true,
	"bos_token_id": 4095,
	"eos_token_id": 4095
	}