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: 1,977 Bytes
68282fa | 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 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | {
"graph_name": "cns_full",
"condition": "real",
"control_seed": 1,
"source": "malecns-v1.0",
"region_filter": "whole_cns",
"min_synapses": 3,
"selector": "directed_core",
"target_neurons": 200000,
"hash": "d53f26f9bf6f7b8c9944e28fdd4721d2c0a237e7534f9160977c70e6c81ac6c2",
"stats": {
"candidate_pool": 164587,
"candidate_edges": 25563096,
"min_synapses": 3,
"target_neurons": 200000,
"edges_after_threshold": 10510984,
"scc_after_threshold": 160514,
"note": "SCC no larger than target; no pruning or trimming applied",
"core_k": 0,
"final_neurons": 160514,
"final_edges": 10402842,
"source": "malecns-v1.0",
"n_neurons": 160514,
"n_edges": 10402842,
"synaptic_contacts": 102352176.0,
"largest_scc_fraction": 1.0,
"reciprocal_pairs": 1006326,
"in_degree": {
"mean": 64.80956178277285,
"min": 1,
"max": 9193
},
"out_degree": {
"mean": 64.80956178277285,
"min": 1,
"max": 7749
},
"density": 0.00040376518900508274,
"hash": "d53f26f9bf6f7b8c9944e28fdd4721d2c0a237e7534f9160977c70e6c81ac6c2",
"region_breakdown": {
"optic_lobe": 91082,
"central_brain": 36625,
"vnc": 19300,
"other": 13454,
"unannotated": 53
}
},
"diagnostics": {
"provenance": {
"control": "real"
},
"io": {
"n_input": 2048,
"n_output": 4096,
"input_rule": "top_out_degree",
"output_rule": "top_in_degree",
"overlap": 1381
},
"diagnostics": {
"n_neurons": 160514,
"n_edges": 10402842,
"largest_scc_fraction": 1.0,
"reciprocal_pairs": 1006326,
"io_reachable_fraction": 1.0,
"io_path_median": 1.0,
"io_path_p90": 1.0,
"io_path_max": 1.0
},
"gate": {
"microsteps": 2,
"max_p90_hops": 6,
"min_reachable_fraction": 0.95,
"reachable_ok": true,
"p90_ok": true,
"passed": true
}
}
} |