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)# 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: 1,821 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 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 | {
"graph_name": "cb5k",
"condition": "real",
"control_seed": 1,
"source": "malecns-v1.0",
"region_filter": "central_brain",
"min_synapses": 3,
"selector": "directed_core",
"target_neurons": 5000,
"hash": "f82b783b7ccb5a354fc4cf3de6de4a98d75029303c55f8faae28ab807828a007",
"stats": {
"candidate_pool": 37108,
"candidate_edges": 7294218,
"min_synapses": 3,
"target_neurons": 5000,
"edges_after_threshold": 2805370,
"scc_after_threshold": 36461,
"core_size_before_trim": 6576,
"trim_iterations": 1,
"core_k": 40,
"final_neurons": 5000,
"final_edges": 524324,
"source": "malecns-v1.0",
"n_neurons": 5000,
"n_edges": 524324,
"synaptic_contacts": 8300915.0,
"largest_scc_fraction": 1.0,
"reciprocal_pairs": 93055,
"in_degree": {
"mean": 104.8648,
"min": 4,
"max": 881
},
"out_degree": {
"mean": 104.8648,
"min": 5,
"max": 1539
},
"density": 0.020977155431086216,
"hash": "f82b783b7ccb5a354fc4cf3de6de4a98d75029303c55f8faae28ab807828a007",
"region_breakdown": {
"central_brain": 5000
}
},
"diagnostics": {
"provenance": {
"control": "real"
},
"io": {
"n_input": 256,
"n_output": 512,
"input_rule": "top_out_degree",
"output_rule": "top_in_degree",
"overlap": 160
},
"diagnostics": {
"n_neurons": 5000,
"n_edges": 524324,
"largest_scc_fraction": 1.0,
"reciprocal_pairs": 93055,
"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
}
}
} |