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
scrambled control seed 1, 100k steps (best val 1.5816)
Browse files- scrambled/README.md +13 -12
- scrambled/config.json +5 -4
- scrambled/model.safetensors +1 -1
scrambled/README.md
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@@ -24,7 +24,7 @@ connectivity tables. FlyGPT's graph is extracted from it deterministically (`bui
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This checkpoint's wiring is a degree-preserving scramble of the MaleCNS wiring (control seed 1; edge survival 0.042).
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**Trained.** Condition `degree_preserving`, seed 1, step
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This is not a biological simulation of a living fly. The "weights" in the MaleCNS release are anatomical synapse
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counts; they are stored here as `graph.synapse_count` and are **not** the model's parameters.
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| seed | real | degree-preserving scramble | Δ |
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| **mean** | **1.
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Validation loss in nats/char at the end of
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stream per seed. Bigram reference on this split: 2.482.
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## Citation
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This checkpoint's wiring is a degree-preserving scramble of the MaleCNS wiring (control seed 1; edge survival 0.042).
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**Trained.** Condition `degree_preserving`, seed 1, step 94000, validation loss 1.5816 nats/char on the fixed Tiny Shakespeare split.
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This is not a biological simulation of a living fly. The "weights" in the MaleCNS release are anatomical synapse
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counts; they are stored here as `graph.synapse_count` and are **not** the model's parameters.
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| seed | real | degree-preserving scramble | Δ |
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| 1 | 1.6044 | 1.6125 | +0.0081 |
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| 2 | 1.6193 | 1.6299 | +0.0106 |
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| 3 | 1.6031 | 1.6064 | +0.0033 |
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| 4 | 1.6083 | 1.6065 | -0.0018 |
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| 5 | 1.6114 | 1.6073 | -0.0041 |
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| **mean** | **1.6093** | **1.6125** | **+0.0032** |
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Validation loss in nats/char at the end of 100,000 steps, same data order, batches, adapter init and edge-value RNG
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stream per seed. Bigram reference on this split: 2.482. The differences are small and not all of the same sign, so the
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verdict is: **no detectable difference at this scale.** The fly connectome learns Shakespeare; at 5,000 neurons its specific wiring does not
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measurably beat a degree-matched scramble. (At 20,000 steps the real wiring led on all five seeds by a mean of
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0.011 nats; with full training the scramble catches up, so that early edge is a learning-speed effect.)
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## Citation
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scrambled/config.json
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"degree_normalization": true,
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"init_scale": 1.0,
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"dtype": "bfloat16",
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"graph": {
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"graph_name": "cb5k",
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"condition": "degree_preserving",
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"status": "trained",
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"condition": "degree_preserving",
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"seed": 1,
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"step":
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"val_loss": 1.
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"checkpoint": "checkpoints/flygpt-v0/cb5k/degree_preserving_seed1.pt",
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"project": "flygpt-v0"
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}
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}
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"degree_normalization": true,
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"init_scale": 1.0,
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"dtype": "bfloat16",
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"training_steps": 100000,
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"graph": {
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"graph_name": "cb5k",
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"condition": "degree_preserving",
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"status": "trained",
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"condition": "degree_preserving",
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"seed": 1,
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"step": 94000,
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"val_loss": 1.5815802812576294,
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"checkpoint": "checkpoints/flygpt-v0-100k/cb5k/degree_preserving_seed1.pt",
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"project": "flygpt-v0-100k"
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}
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}
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scrambled/model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 7495610
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version https://git-lfs.github.com/spec/v1
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size 7495610
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