Text Generation
Transformers
Safetensors
English
flygpt
connectome
fruit-fly
drosophila
malecns
recurrent
sparse
tiny-shakespeare
custom_code
Instructions to use QuixiAI/FlyGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuixiAI/FlyGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuixiAI/FlyGPT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("QuixiAI/FlyGPT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QuixiAI/FlyGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuixiAI/FlyGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuixiAI/FlyGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuixiAI/FlyGPT
- SGLang
How to use QuixiAI/FlyGPT 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 "QuixiAI/FlyGPT" \ --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": "QuixiAI/FlyGPT", "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 "QuixiAI/FlyGPT" \ --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": "QuixiAI/FlyGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use QuixiAI/FlyGPT with Docker Model Runner:
docker model run hf.co/QuixiAI/FlyGPT
File size: 1,481 Bytes
9b51f42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | """FlyGPT configuration for Hugging Face `transformers` (loaded with trust_remote_code=True)."""
from transformers import PretrainedConfig
class FlyGPTConfig(PretrainedConfig):
model_type = "flygpt"
def __init__(
self,
vocab_size: int = 65,
num_neurons: int = 0,
num_edges: int = 0,
embedding_dim: int = 32,
num_input_neurons: int = 256,
num_output_neurons: int = 512,
microsteps: int = 2,
activation: str = "tanh",
learned_leak: bool = True,
leak_init: float = 0.5,
degree_normalization: bool = True,
init_scale: float = 0.1,
graph: dict | None = None,
training_state: dict | None = None,
**kwargs,
):
self.vocab_size = vocab_size
self.num_neurons = num_neurons
self.num_edges = num_edges
self.embedding_dim = embedding_dim
self.num_input_neurons = num_input_neurons
self.num_output_neurons = num_output_neurons
self.microsteps = microsteps
self.activation = activation
self.learned_leak = learned_leak
self.leak_init = leak_init
self.degree_normalization = degree_normalization
self.init_scale = init_scale
self.graph = graph or {} # source, hash, region filter, stats, provenance
self.training_state = training_state or {} # "init" or the run that produced the values
super().__init__(**kwargs)
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