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 / config.json

raincandy-u

Release MacroStories: 19,969-parameter story model and model card

a142550 verified 13 days ago

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1.23 kB

	{
	"architectures": [
	"OuroForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_ouro.OuroConfig",
	"AutoModelForCausalLM": "modeling_ouro.OuroForCausalLM"
	},
	"bos_token_id": 1,
	"early_exit_threshold": null,
	"embedding_size": 32,
	"eos_token_id": 2,
	"ffn_gated": true,
	"hidden_act": "silu",
	"hidden_size": 32,
	"initializer_range": 0.02,
	"input_embedding_injection_coefficient": 0.0,
	"intermediate_size": 48,
	"layer_types": [
	"full_attention"
	],
	"loop_aux_loss_weight": 0.0,
	"max_position_embeddings": 2048,
	"max_window_layers": 28,
	"model_type": "ouro",
	"num_attention_heads": 2,
	"num_hidden_layers": 1,
	"num_key_value_heads": 1,
	"pad_token_id": 0,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"sliding_window": null,
	"student_architecture_source": "ByteDance/Ouro-1.4B@574fa66cb8bf5abdc979642d01cf2b79b16bfab1",
	"student_loss_mode": "last_loop",
	"tie_word_embeddings": true,
	"torch_dtype": "float32",
	"total_ut_steps": 4,
	"transformers_version": "4.55.0",
	"use_cache": true,
	"use_loop_embeddings": false,
	"use_sliding_window": false,
	"value_residual_mix": 0.5,
	"vocab_size": 378
	}