Instructions to use raincandy-u/MacroStories with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raincandy-u/MacroStories with Transformers:

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

pipe = pipeline("text-generation", model="raincandy-u/MacroStories", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("raincandy-u/MacroStories", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raincandy-u/MacroStories with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/raincandy-u/MacroStories

SGLang

How to use raincandy-u/MacroStories 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 "raincandy-u/MacroStories" \
    --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": "raincandy-u/MacroStories",
		"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 "raincandy-u/MacroStories" \
        --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": "raincandy-u/MacroStories",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raincandy-u/MacroStories with Docker Model Runner:
```
docker model run hf.co/raincandy-u/MacroStories
```

MacroStories

File size: 423 Bytes

a142550

{
  "architecture": "ouro",
  "num_hidden_layers": 1,
  "num_attention_heads": 2,
  "loops": 4,
  "optimizer": "muon",
  "learning_rate": 0.003,
  "batch_size": 32,
  "eval_batch_size": 32,
  "max_steps": 4000,
  "eval_steps": 250,
  "seed": 456,
  "data_seed": 42,
  "hidden_size": 32,
  "intermediate_size": 48,
  "model_overrides": {
    "value_residual_mix": 0.5,
    "num_key_value_heads": 1
  }
}