Instructions to use appvoid/cortex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/cortex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/cortex", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("appvoid/cortex", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/cortex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/cortex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/cortex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/cortex
- SGLang
How to use appvoid/cortex 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 "appvoid/cortex" \ --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": "appvoid/cortex", "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 "appvoid/cortex" \ --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": "appvoid/cortex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/cortex with Docker Model Runner:
docker model run hf.co/appvoid/cortex
unified-v1-evabyte-none-s42: state and inference synchronized at checkpoint-000000000100
Browse files
experiments.csv
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unified-v1-evabyte-none-s42,ablation,causal,evabyte,none,42,75,0,running,fresh_matched_seed,5450,5450,1.026456674999963,0.0,10668800,1024,2.74536669254303,6.275834560394287,3637662,29.178130834000626,124670.83723406687,0.1350708305835724,0,bf16,0,269ba207e744573779747b593ffbcd8a87683fd55c83f6b168c658206d9aeb98,af33c522ea4349d5aae0491f7fe085d4bea1b044b08ff57eb064354bbb96435f,"{""climbmix"": {""documents_emitted"": 750, ""epoch"": 0, ""prepared_position"": 2482, ""rows_read"": 751, ""shard"": 0, ""shards"": 6543}, ""rewrite6"": {""documents_emitted"": 307, ""epoch"": 0, ""prepared_position"": 405, ""rows_read"": 308, ""shard"": 0, ""shards"": 1}, ""ultra_qa"": {""documents_emitted"": 163, ""epoch"": 0, ""prepared_position"": 712, ""rows_read"": 164, ""shard"": 0, ""shards"": 616}, ""ultra_style"": {""documents_emitted"": 204, ""epoch"": 0, ""prepared_position"": 720, ""rows_read"": 205, ""shard"": 0, ""shards"": 552}}"
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unified-v1-evabyte-none-s42,ablation,causal,evabyte,none,42,100,0,
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unified-v1-evabyte-none-s42,ablation,causal,evabyte,none,42,25,0,running,fresh_matched_seed,1826,1826,1.026456674999963,0.0,10668800,1024,3.2617745995521545,,1213274,10.331318205000343,117436.51448203165,0.17493173480033875,0,bf16,0,269ba207e744573779747b593ffbcd8a87683fd55c83f6b168c658206d9aeb98,af33c522ea4349d5aae0491f7fe085d4bea1b044b08ff57eb064354bbb96435f,"{""climbmix"": {""documents_emitted"": 253, ""epoch"": 0, ""prepared_position"": 829, ""rows_read"": 253, ""shard"": 0, ""shards"": 6543}, ""rewrite6"": {""documents_emitted"": 121, ""epoch"": 0, ""prepared_position"": 145, ""rows_read"": 122, ""shard"": 0, ""shards"": 1}, ""ultra_qa"": {""documents_emitted"": 55, ""epoch"": 0, ""prepared_position"": 237, ""rows_read"": 56, ""shard"": 0, ""shards"": 616}, ""ultra_style"": {""documents_emitted"": 73, ""epoch"": 0, ""prepared_position"": 243, ""rows_read"": 74, ""shard"": 0, ""shards"": 552}}"
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unified-v1-evabyte-none-s42,ablation,causal,evabyte,none,42,75,0,running,fresh_matched_seed,5450,5450,1.026456674999963,0.0,10668800,1024,2.74536669254303,6.275834560394287,3637662,29.178130834000626,124670.83723406687,0.1350708305835724,0,bf16,0,269ba207e744573779747b593ffbcd8a87683fd55c83f6b168c658206d9aeb98,af33c522ea4349d5aae0491f7fe085d4bea1b044b08ff57eb064354bbb96435f,"{""climbmix"": {""documents_emitted"": 750, ""epoch"": 0, ""prepared_position"": 2482, ""rows_read"": 751, ""shard"": 0, ""shards"": 6543}, ""rewrite6"": {""documents_emitted"": 307, ""epoch"": 0, ""prepared_position"": 405, ""rows_read"": 308, ""shard"": 0, ""shards"": 1}, ""ultra_qa"": {""documents_emitted"": 163, ""epoch"": 0, ""prepared_position"": 712, ""rows_read"": 164, ""shard"": 0, ""shards"": 616}, ""ultra_style"": {""documents_emitted"": 204, ""epoch"": 0, ""prepared_position"": 720, ""rows_read"": 205, ""shard"": 0, ""shards"": 552}}"
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unified-v1-evabyte-none-s42,ablation,causal,evabyte,none,42,100,0,complete,fresh_matched_seed,7230,7230,1.026456674999963,0.0,10668800,1024,2.70014488697052,5.0743002961663635,4841990,38.114180579000845,127039.06856829327,0.1096256747841835,0,bf16,0,269ba207e744573779747b593ffbcd8a87683fd55c83f6b168c658206d9aeb98,af33c522ea4349d5aae0491f7fe085d4bea1b044b08ff57eb064354bbb96435f,"{""climbmix"": {""documents_emitted"": 989, ""epoch"": 0, ""prepared_position"": 3299, ""rows_read"": 990, ""shard"": 0, ""shards"": 6543}, ""rewrite6"": {""documents_emitted"": 389, ""epoch"": 0, ""prepared_position"": 522, ""rows_read"": 390, ""shard"": 0, ""shards"": 1}, ""ultra_qa"": {""documents_emitted"": 217, ""epoch"": 0, ""prepared_position"": 950, ""rows_read"": 218, ""shard"": 0, ""shards"": 616}, ""ultra_style"": {""documents_emitted"": 273, ""epoch"": 0, ""prepared_position"": 959, ""rows_read"": 274, ""shard"": 0, ""shards"": 552}}"
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unified/ablations/unified-v1-evabyte-none-s42/inference/data_progress.json
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"recipe": "794a6de09517a367ca643fda6b9a2a511c975f0a62cb0aa3fbdd0321a6a34e80"
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"observations": {
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"step": 100,
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"revision": "ba73f1c94be3dcbb33ffee4091b2303dadd29ad3",
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"recipe": "794a6de09517a367ca643fda6b9a2a511c975f0a62cb0aa3fbdd0321a6a34e80"
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unified/ablations/unified-v1-evabyte-none-s42/inference/experiment_config.json
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"step": 100,
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"revision": "
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"recipe": "794a6de09517a367ca643fda6b9a2a511c975f0a62cb0aa3fbdd0321a6a34e80"
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"observations": {
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"step": 100,
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"prepared_dataset": {
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"repo": "appvoid/rewrite7",
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"revision": "ba73f1c94be3dcbb33ffee4091b2303dadd29ad3",
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"recipe": "794a6de09517a367ca643fda6b9a2a511c975f0a62cb0aa3fbdd0321a6a34e80"
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},
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"observations": {
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